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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"nds\" data-health-profile=\"biomedical-research\">\n<div class=\"nds-wrap\">\n<section class=\"nds-executive\"><h2>Studying mortality after heart failure<\/h2><p>Can recorded clinical measurements distinguish patients who died during follow-up from those who did not? This example trains an 11-input classifier in Neural Designer and examines its discrimination, missed events and false positives on a held-out test subset.<\/p><div class=\"nds-kpis\"><div class=\"nds-kpi\"><strong>299<\/strong><span>Source records<\/span><\/div><div class=\"nds-kpi\"><strong>11<\/strong><span>Final input features<\/span><\/div><div class=\"nds-kpi\"><strong>59<\/strong><span>Test observations<\/span><\/div><div class=\"nds-kpi\"><strong>0.776<\/strong><span>Test ROC AUC<\/span><\/div><\/div><div class=\"nds-actions\"><a class=\"aui aui-button\" href=\"#6-clinical-validation\">Explore the results<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/heart-failure-data-20260921.csv\">Review the data<\/a><\/div><\/section>\n<ul class=\"nds-toc\"><li><a href=\"#1-intended-use\">Question and use<\/a><\/li><li><a href=\"#2-cohort-endpoint\">Cohort and endpoint<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Selection<\/a><\/li><li><a href=\"#6-clinical-validation\">Validation<\/a><\/li><li><a href=\"#7-workflow\">Workflow<\/a><\/li><li><a href=\"#8-safety\">Safety and validity<\/a><\/li><\/ul>\n<span id=\"ApplicationType\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"1-intended-use\" class=\"nds-card\"><h2>1. Research question and intended use<\/h2><p>The modelling question concerns the recorded outcome in this cohort. It is a reproducible classification exercise for studying clinical data, with explicit attention to score direction and the errors at a chosen threshold.<\/p><div class=\"nds-value-grid\"><div class=\"nds-value\"><h3>Recorded outcome<\/h3><p>DEATH_EVENT=1 identifies a death observed during follow-up.<\/p><\/div><div class=\"nds-value\"><h3>Clinical measurements<\/h3><p>The model uses 11 demographic, laboratory and clinical inputs.<\/p><\/div><div class=\"nds-value\"><h3>Held-out evidence<\/h3><p>Inspect the ROC curve alongside the 8 missed deaths and 7 false positives at a threshold of 0.5.<\/p><\/div><\/div><div class=\"nds-audience\"><span>Clinical-data research<\/span><span>Model evaluation<\/span><span>Health analytics<\/span><\/div><div class=\"nds-note nds-use-boundary\">Research demonstration: the score describes this recorded binary outcome and does not establish an individual treatment decision.<\/div><\/section>\n<span id=\"DataSet\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"2-cohort-endpoint\" class=\"nds-card\"><h2>2. Cohort, measurements and endpoint<\/h2><p>The prepared Heart Failure Clinical Records table contains 299 patient records, with 96 recorded deaths and 203 records without a death event. The endpoint is DEATH_EVENT: 1 means death observed during follow-up; 0 means no death observed during that period. The prepared CSV has 11 predictors and excludes the original follow-up-time column. Follow-up duration varies in the source cohort, so this binary endpoint is not a common fixed-horizon mortality outcome.<\/p><p>The downloadable project, saved report and supplied source CSV define the exact version used here. Repository: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/519\/heart+failure+clinical+records\">original dataset\/source record<\/a>. <\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">Records<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>181<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>59<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>59<\/td><\/tr><tr><th scope=\"row\">Unused<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Variable<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Encoding<\/th><th scope=\"col\">Unit<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>years<\/td><\/tr><tr><th scope=\"row\">anaemia<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0=no; 1=yes<\/td><\/tr><tr><th scope=\"row\">creatinine_phosphokinase<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>mcg\/L (source convention)<\/td><\/tr><tr><th scope=\"row\">diabetes<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0=no; 1=yes<\/td><\/tr><tr><th scope=\"row\">ejection_fraction<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>%<\/td><\/tr><tr><th scope=\"row\">high_blood_pressure<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0=no; 1=yes<\/td><\/tr><tr><th scope=\"row\">platelets<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>kiloplatelets\/mL (source convention)<\/td><\/tr><tr><th scope=\"row\">serum_creatinine<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>mg\/dL<\/td><\/tr><tr><th scope=\"row\">serum_sodium<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>mEq\/L<\/td><\/tr><tr><th scope=\"row\">sex<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0=woman; 1=man<\/td><\/tr><tr><th scope=\"row\">smoking<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0=no; 1=yes<\/td><\/tr><tr><th scope=\"row\">DEATH_EVENT<\/th><td>Target<\/td><td>Binary<\/td><td>0; 1<\/td><td>0=no recorded death; 1=recorded death<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-heart-failure-t0-s17-20260921\"><div class=\"nd-chart-host\" id=\"nd-heart-failure-t0-s17-20260921\" role=\"img\" aria-label=\"DEATH_EVENT pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: DEATH_EVENT pie chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"0\",\"1\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[67.9000015258789,32.099998474121094]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"DEATH_EVENT pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>DEATH_EVENT pie chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-heart-failure-t2-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-heart-failure-t2-s1-20260921\" role=\"img\" aria-label=\"DEATH_EVENT Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: DEATH_EVENT Pearson correlations chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"orientation\":\"h\",\"showlegend\":false,\"type\":\"bar\",\"x\":[-0.33000001311302185,-0.20999999344348907,-0.050599999725818634,-0.012600000016391277,-0.00431999983265996,0.06610000133514404,0.06629999727010727,0.07940000295639038,0.2619999945163727,0.35899999737739563],\"y\":[\"ejection_fraction\",\"serum_sodium\",\"platelets\",\"smoking\",\"sex\",\"creatinine_phosphokinase\",\"anaemia\",\"high_blood_pressure\",\"age\",\"serum_creatinine\"]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"DEATH_EVENT Pearson correlations chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-1,1],\"title\":{\"text\":\"Correlation\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"autorange\":\"reversed\",\"categoryarray\":[\"ejection_fraction\",\"serum_sodium\",\"platelets\",\"smoking\",\"sex\",\"creatinine_phosphokinase\",\"anaemia\",\"high_blood_pressure\",\"age\",\"serum_creatinine\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>DEATH_EVENT Pearson correlations chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><div class=\"nds-note nds-note--provenance\">The downloadable project, saved report and supplied source CSV define the exact version used here. Repository: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/519\/heart+failure+clinical+records\">original dataset\/source record<\/a>. This is internal validation using the saved record-level split. Grouped or temporal independence has not been established.<\/div><\/section>\n<span id=\"NeuralNetwork\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"3-model\" class=\"nds-card\"><h2>3. Model<\/h2><p>The final model has <strong>11 encoded input features<\/strong> and <strong>1 outputs<\/strong>. The following dimensions describe the final saved network.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>11<\/td><td>11<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>11<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output semantics: the sigmoid score increases toward <strong>1<\/strong>; 0 is the other class. Calibration has not been evaluated, so scores are not presented as calibrated probabilities.<\/p><figure class=\"nds-architecture-figure\" data-model-stage=\"initial\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-heart-failure-t3-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-heart-failure-t3-s1-20260921.png\" alt=\"Studying mortality after heart failure: initial Neural Designer architecture\"><\/a><figcaption>Architecture used for this model; no architecture-selection experiment is recorded. The model has 11 inputs, a hidden layer with 3 tanh neurons and one sigmoid output.<\/figcaption><\/figure><\/section>\n<span id=\"TrainingStrategy\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"4-training\" class=\"nds-card\"><h2>4. Training strategy<\/h2><p>Training uses weighted squared error, L2 regularization (weight 0.01) and the Quasi-Newton optimizer. Training minimizes the recorded objective; the validation subset monitors generalization during fitting. The testing subset is used for the evaluation below.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-heart-failure-t4-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-heart-failure-t4-s2-20260921\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Quasi-Newton method error history. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training 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error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110],\"y\":[0.8989999890327454,0.8920000195503235,0.8370000123977661,0.7799999713897705,0.7739999890327454,0.8209999799728394,0.8059999942779541,0.7699999809265137,0.7509999871253967,0.7419999837875366,0.7409999966621399,0.7409999966621399,0.7490000128746033,0.746999979019165,0.75,0.753000020980835,0.7509999871253967,0.7459999918937683,0.7459999918937683,0.7490000128746033,0.7519999742507935,0.7549999952316284,0.7559999823570251,0.7559999823570251,0.7549999952316284,0.7549999952316284,0.7540000081062317,0.7590000033378601,0.7639999985694885,0.7710000276565552,0.7829999923706055,0.7870000004768372,0.7919999957084656,0.7960000038146973,0.7929999828338623,0.7879999876022339,0.7860000133514404,0.781000018119812,0.7879999876022339,0.7929999828338623,0.7990000247955322,0.7940000295639038,0.7919999957084656,0.7950000166893005,0.8040000200271606,0.8149999976158142,0.8349999785423279,0.8410000205039978,0.8450000286102295,0.8360000252723694,0.8389999866485596,0.8410000205039978,0.843999981880188,0.8429999947547913,0.843999981880188,0.8450000286102295,0.8460000157356262,0.8460000157356262,0.8460000157356262,0.8460000157356262,0.847000002861023,0.8479999899864197,0.8500000238418579,0.8519999980926514,0.8529999852180481,0.8539999723434448,0.8550000190734863,0.8529999852180481,0.8500000238418579,0.8450000286102295,0.8420000076293945,0.8370000123977661,0.8399999737739563,0.8399999737739563,0.8379999995231628,0.8309999704360962,0.8199999928474426,0.8069999814033508,0.8080000281333923,0.8180000185966492,0.843999981880188,0.8149999976158142,0.8169999718666077,0.8220000267028809,0.8320000171661377,0.843999981880188,0.8560000061988831,0.8610000014305115,0.8659999966621399,0.8709999918937683,0.8709999918937683,0.8700000047683716,0.8659999966621399,0.8619999885559082,0.8550000190734863,0.8489999771118164,0.847000002861023,0.8489999771118164,0.8489999771118164,0.8479999899864197,0.8479999899864197,0.847000002861023,0.847000002861023,0.847000002861023,0.847000002861023,0.847000002861023,0.847000002861023,0.8460000157356262,0.8460000157356262,0.847000002861023,0.847000002861023]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Quasi-Newton method error history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,110],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Quasi-Newton method error history. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>111<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:00<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum validation error increases<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.592<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.741<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<span id=\"ModelSelection\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"5-selection\" class=\"nds-card\"><h2>5. Model selection and baseline<\/h2><p>No model selection experiment is recorded in this supplied project. The displayed architecture is the trained model used for testing; earlier article claims about a different selected architecture do not apply to this version.<\/p><p>A transparent test-set comparator is the majority-class rule, with accuracy 67.8%. This is a baseline for interpretation, not an alternative model fitted on the test labels.<\/p><\/section>\n<span id=\"TestingAnalysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"6-clinical-validation\" class=\"nds-card\"><h2>6. Testing and interpretation<\/h2><p>In the test subset, 19 of 59 patients have DEATH_EVENT=1 (32.2%). At the 0.5 threshold, the model identifies 11 of these events and misses 8; it also flags 7 of the 40 records without a death event. For the death-event class, sensitivity is 57.9%, specificity 82.5% and positive predictive value 61.1%.<\/p><p>The final classifier is evaluated on <strong>59 testing records<\/strong>. The confusion counts below were reproduced from the saved model. Rows are actual classes and columns are predicted classes. The decision threshold is 0.5 on the score for 1.<\/p><p>Test class prevalence is shown by the support counts. Accuracy is 74.6% and macro F1 is 0.705. ROC AUC is 0.776. The native ROC optimal threshold is descriptive of this test set and is not an independently validated operating policy.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Actual \/ predicted<\/th><th scope=\"col\">0<\/th><th scope=\"col\">1<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>33<\/td><td>7<\/td><td>40<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>8<\/td><td>11<\/td><td>19<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Test cases<\/th><th scope=\"col\">Sensitivity \/ recall<\/th><th scope=\"col\">Specificity<\/th><th scope=\"col\">Precision \/ PPV<\/th><th scope=\"col\">F1<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>40<\/td><td>82.5%<\/td><td>57.9%<\/td><td>80.5%<\/td><td>0.815<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>19<\/td><td>57.9%<\/td><td>82.5%<\/td><td>61.1%<\/td><td>0.595<\/td><\/tr><\/tbody><\/table><\/div><p>The native ROC task reports AUC 0.776 with 90% confidence limits 0.661\u20130.891. Its test-derived threshold of 0.382 gives sensitivity 73.7% and specificity 75.0% on these same cases. This exploratory cutoff was not selected on an independent validation set; the confusion matrix above retains the default threshold of 0.5.<\/p><div class=\"nd-native-test-figures\"><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-heart-failure-t5-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-heart-failure-t5-s1-20260921\" role=\"img\" aria-label=\"ROC chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: ROC chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"fill\":\"tozeroy\",\"fillcolor\":\"rgba(82,156,194,0.300)\",\"line\":{\"color\":\"#ffffff\",\"dash\":\"solid\",\"width\":1},\"marker\":{\"color\":\"#529cc2\",\"size\":5,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Random classifier\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1,0.9750000238418579,0.949999988079071,0.925000011920929,0.8999999761581421,0.875,0.8500000238418579,0.8500000238418579,0.824999988079071,0.800000011920929,0.7749999761581421,0.75,0.7250000238418579,0.699999988079071,0.675000011920929,0.6499999761581421,0.625,0.625,0.625,0.6000000238418579,0.574999988079071,0.550000011920929,0.5249999761581421,0.5,0.4749999940395355,0.44999998807907104,0.42500001192092896,0.4000000059604645,0.4000000059604645,0.4000000059604645,0.375,0.3499999940395355,0.32499998807907104,0.30000001192092896,0.2750000059604645,0.25,0.25,0.22499999403953552,0.22499999403953552,0.22499999403953552,0.20000000298023224,0.17499999701976776,0.15000000596046448,0.125,0.125,0.10000000149011612,0.07500000298023224,0.07500000298023224,0.07500000298023224,0.05000000074505806,0.05000000074505806,0.05000000074505806,0.05000000074505806,0.05000000074505806,0.05000000074505806,0.05000000074505806,0.05000000074505806,0.02500000037252903,0.02500000037252903,0],\"y\":[1,1,1,1,1,1,1,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.9473680257797241,0.8947370052337646,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.842104971408844,0.7894740104675293,0.7368419766426086,0.7368419766426086,0.7368419766426086,0.7368419766426086,0.7368419766426086,0.7368419766426086,0.7368419766426086,0.684211015701294,0.684211015701294,0.6315789818763733,0.5789470076560974,0.5789470076560974,0.5789470076560974,0.5789470076560974,0.5789470076560974,0.5263159871101379,0.5263159871101379,0.5263159871101379,0.47368401288986206,0.4210529923439026,0.4210529923439026,0.3684209883213043,0.31578901410102844,0.26315799355506897,0.2105260044336319,0.15789499878883362,0.10526300221681595,0.05263160169124603,0.05263160169124603,0,0]},{\"connectgaps\":false,\"line\":{\"color\":\"#808080\",\"dash\":\"solid\",\"width\":1},\"mode\":\"lines\",\"name\":\"True Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,1],\"y\":[0,1]},{\"connectgaps\":false,\"marker\":{\"color\":\"#ed982a\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"True Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0.25],\"y\":[0.7368419766426086]}],\"layout\":{\"annotations\":[{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Optimal Threshold\",\"x\":0.42000000000000004,\"y\":0.6468419766426087},{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Area under curve: 0.776\",\"x\":0.7,\"y\":0.02}],\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"ROC chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"False Positive Rate (1-specificity)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"True Positive Rate (sensitivity)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>ROC chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><\/div><div class=\"nds-note\">This is internal record-level evidence. Discrimination does not establish calibration, clinical utility or benefit to patients.<\/div><\/section>\n<span id=\"ModelDeployment\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"7-workflow\" class=\"nds-card\"><h2>7. Workflow and reproducibility<\/h2><p>Check inputs \u2192 apply the saved preprocessing and network \u2192 obtain a DEATH_EVENT score \u2192 review the research result. The ZIP contains the original project, source CSV, schema, test metrics and standalone interactive chart exports. The project hash in the schema identifies this exact version.<\/p><div class=\"nd-model-calculator\" id=\"nd-model-heart-failure\"><h3>Explore the exported model<\/h3><p>This research demonstration runs locally in your browser. Values outside the saved input range are outside the validated domain and are rejected. Plausible individual values do not guarantee that their combination is represented in the cohort.<\/p><p>The output is an uncalibrated score for DEATH_EVENT=1, not a personal mortality probability. It must not guide medical treatment.<\/p><div class=\"nd-model-fields\"><\/div><div class=\"nd-model-actions\"><button class=\"aui aui-button nd-model-calculate\" type=\"button\">Calculate<\/button><button class=\"aui aui-button aui-button--secondary nd-model-reset\" type=\"button\">Reset example<\/button><\/div><div class=\"nd-model-result\" role=\"status\" aria-live=\"polite\"><\/div><\/div><script data-noptimize=\"1\">(()=>{function Identity(x) {\n\treturn x;\n}\nfunction Sigmoid(x) {\n\tvar z = 1\/(1+Math.exp(-x));\n\treturn z;\n}\nfunction Tanh(x) {\n\treturn Math.tanh(x);\n}\nfunction calculate_outputs(inputs)\n{\n\tvar age = +inputs[0];\n\tvar anaemia = +inputs[1];\n\tvar creatinine_phosphokinase = +inputs[2];\n\tvar diabetes = +inputs[3];\n\tvar ejection_fraction = +inputs[4];\n\tvar high_blood_pressure = +inputs[5];\n\tvar platelets = +inputs[6];\n\tvar serum_creatinine = +inputs[7];\n\tvar serum_sodium = +inputs[8];\n\tvar sex = +inputs[9];\n\tvar smoking = +inputs[10];\n\n\tvar scaled_age = age*0.08421123773-5.122898102;\n\tvar scaled_anaemia = anaemia*2.0-1.0;\n\tvar scaled_creatinine_phosphokinase = creatinine_phosphokinase*0.001032349654-0.6006612778;\n\tvar scaled_diabetes = diabetes*2.0-1.0;\n\tvar scaled_ejection_fraction = ejection_fraction*0.08463817835-3.223326445;\n\tvar scaled_high_blood_pressure = high_blood_pressure*2.0-1.0;\n\tvar scaled_platelets = platelets*1.024164067e-05-2.697217941;\n\tvar scaled_serum_creatinine = serum_creatinine*0.9682603478-1.3496387;\n\tvar scaled_serum_sodium = serum_sodium*0.2270101309-31.01525879;\n\tvar scaled_sex = sex*2.0-1.0;\n\tvar scaled_smoking = smoking*2.0-1.0;\n\tvar dense_layer_1_output_0 = Tanh( -0.0910513103 + (-0.1329077631*scaled_age) + (-0.04663843662*scaled_anaemia) + (0.1712186784*scaled_creatinine_phosphokinase) + (0.1221916601*scaled_diabetes) + (-0.0702296719*scaled_ejection_fraction) + (0.1042668149*scaled_high_blood_pressure) + (0.1950132698*scaled_platelets) + (0.2855474651*scaled_serum_creatinine) + (0.251132071*scaled_serum_sodium) + (-0.0442205444*scaled_sex) + (0.1948586553*scaled_smoking) );\n\tvar dense_layer_1_output_1 = Tanh( 0.1361228228 + (-0.08925447613*scaled_age) + (-0.1375907809*scaled_anaemia) + (-0.1763121039*scaled_creatinine_phosphokinase) + (-0.02398810536*scaled_diabetes) + (0.4693935513*scaled_ejection_fraction) + (-0.05918489769*scaled_high_blood_pressure) + (0.06738095731*scaled_platelets) + (-0.5219885707*scaled_serum_creatinine) + (0.2219297886*scaled_serum_sodium) + (0.1240713*scaled_sex) + (-0.1433055401*scaled_smoking) );\n\tvar dense_layer_1_output_2 = Tanh( -0.1728625298 + (0.06801211834*scaled_age) + (0.1480717659*scaled_anaemia) + (0.2081854045*scaled_creatinine_phosphokinase) + (0.1049084812*scaled_diabetes) + (-0.8417879939*scaled_ejection_fraction) + (0.1561429352*scaled_high_blood_pressure) + (-0.03811579198*scaled_platelets) + (0.7617527246*scaled_serum_creatinine) + (-0.2220919877*scaled_serum_sodium) + (-0.1874582767*scaled_sex) + (0.1898447871*scaled_smoking) );\n\tvar DEATH_EVENT = Sigmoid( 0.1862303317 + (-0.08268086612*dense_layer_1_output_0) + (-0.8998359442*dense_layer_1_output_1) + (1.341893077*dense_layer_1_output_2) );\n\tvar out = [];\n\tout.push(DEATH_EVENT);\n\n\treturn out;\n}\nconst cfg={\"fields\":[{\"name\":\"age\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":40.0,\"max\":95.0,\"value\":75.0,\"unit\":\"years\"},{\"name\":\"anaemia\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"0\",\"1\"],\"min\":0.0,\"max\":1.0,\"value\":1,\"unit\":\"0=no; 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1=man\"},{\"name\":\"smoking\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"0\",\"1\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"0=no; 1=yes\"}],\"output_names\":[\"1 score\"],\"reference\":[0.8422563130290879]},root=document.getElementById(\"nd-model-heart-failure\");if(!root)return;const grid=root.querySelector(\".nd-model-fields\"),result=root.querySelector(\".nd-model-result\"),controls=[];cfg.fields.forEach((f,i)=>{const wrap=document.createElement(\"div\");wrap.className=\"nd-model-field\";const label=document.createElement(\"label\"),id=root.id+\"-\"+i;label.htmlFor=id;label.textContent=f.name.replaceAll(\"_\",\" \")+(f.unit?\" (\"+f.unit+\")\":\"\");let input;if(f.categories?.length){input=document.createElement(\"select\");f.categories.forEach((name,k)=>{const option=document.createElement(\"option\");option.value=String(k);option.textContent=name;input.appendChild(option);});}else{input=document.createElement(\"input\");input.type=\"number\";input.step=\"any\";input.min=String(f.min);input.max=String(f.max);}input.id=id;input.value=String(f.value);wrap.append(label,input);grid.appendChild(wrap);controls.push(input);});const calculate=()=>{const inputs=[];let valid=true;cfg.fields.forEach((f,i)=>{const el=controls[i],v=Number(el.value);let bad=el.value.trim()===\"\"||!Number.isFinite(v);if(!f.categories?.length)bad=bad||v<f.min||v>f.max;el.setAttribute(\"aria-invalid\",bad?\"true\":\"false\");if(bad)valid=false;if(f.n>1){for(let k=0;k<f.n;k++)inputs.push(k===v?1:0);}else inputs.push(v);});if(!valid){result.textContent=\"Enter a valid value within every displayed input range.\";return;}let outputs=calculate_outputs(inputs);if(outputs.some(v=>!Number.isFinite(v))){result.textContent=\"The model could not evaluate these inputs.\";return;}result.textContent=outputs.map((v,i)=>cfg.output_names[i]+\": \"+Number(v.toPrecision(7))).join(\"\\n\");root.dataset.outputs=JSON.stringify(outputs);};root.querySelector(\".nd-model-calculate\").addEventListener(\"click\",calculate);root.querySelector(\".nd-model-reset\").addEventListener(\"click\",()=>{controls.forEach((el,i)=>el.value=String(cfg.fields[i].value));calculate();});calculate();})();<\/script><div class=\"nds-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/heart-failure-neural-designer-20260921.zip\">Download project and exports (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/heart-failure-data-20260921.csv\">Download source data (CSV)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/heart-failure-schema-20260921.zip\">Download input schema (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Reproduce with Neural Designer<\/a><\/div><\/section>\n<section id=\"8-safety\" class=\"nds-card\"><h2>8. Safety, generalizability and governance<\/h2><p>The 59-record test subset is small and comes from the same historical cohort as the training records. This binary classifier does not model event time or censoring and cannot provide a validated 30-day or one-year mortality risk. Before any medical use, establish predictor availability at the assessment time, refit preprocessing within training, evaluate on an independent cohort and obtain clinician review.<\/p><p>No external validation or independent calibration study is included. The saved scaling statistics cover the cohort rather than a separately verified training-only preprocessing fit. Preprocessing statistics and model choices should be refitted within a prospective or grouped validation design. Correlations and directional responses describe associations, not causes. Human review is required before an operational decision.<\/p><p>The saved ROC task provides an AUC confidence interval, but subgroup performance, calibration curves and decision-cost validation are not established by these tasks. Predictive values apply to the observed test class distribution and may change when prevalence shifts.<\/p><div class=\"nds-note nds-note--warning\">Expert review and separate external validation are required before any medical use.<\/div><\/section>\n<section id=\"references\" class=\"nds-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/plos.figshare.com\/articles\/dataset\/Survival_analysis_of_heart_failure_patients_A_case_study\/5227684\/1\">Ahmad et al., original cohort data accompanying the 2017 PLOS ONE study<\/a>.<\/li><li><a href=\"https:\/\/doi.org\/10.24432\/C5Z89R\">Heart Failure Clinical Records, UCI Machine Learning Repository (2020)<\/a>, <a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY 4.0<\/a>. This prepared CSV omits follow-up time; the original records are credited to their source.<\/li><\/ul><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/519\/heart+failure+clinical+records\">Dataset source and provenance<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/heart-failure-neural-designer-20260921.zip\">Neural Designer project and saved task evidence, 21 September 2026<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">Neural Designer testing-analysis documentation<\/a>.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const 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ready();})();<\/script>","protected":false},"author":14,"featured_media":2090,"template":"","categories":[29],"tags":[38],"class_list":["post-3391","blog","type-blog","status-publish","has-post-thumbnail","hentry","category-examples","tag-healthcare"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Assess death risk after heart failure using machine learning<\/title>\n<meta name=\"description\" content=\"Based on clinical data, build a machine learning model to assess the death risk of patients who experienced heart failure.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/blog\/heart-failure-death-prediction\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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