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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>Classifying recorded obesity levels<\/h2><p>This research example classifies seven recorded weight-status categories from anthropometric and lifestyle variables. It evaluates multiclass errors rather than treating class codes as a continuous regression target.<\/p><div class=\"nds-kpis\"><div class=\"nds-kpi\"><strong>2,111<\/strong><span>Source records<\/span><\/div><div class=\"nds-kpi\"><strong>20<\/strong><span>Final input features<\/span><\/div><div class=\"nds-kpi\"><strong>422<\/strong><span>Test observations<\/span><\/div><div class=\"nds-kpi\"><strong>0.856<\/strong><span>Test macro F1<\/span><\/div><\/div><div class=\"nds-actions\"><a class=\"aui aui-button\" href=\"#6-clinical-validation\">Explore the model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/obesity-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=\"model_type\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"1-intended-use\" class=\"nds-card\"><h2>1. Clinical question and intended use<\/h2><p>The research question is whether recorded lifestyle and physical characteristics distinguish seven obesity levels. Since height and weight are inputs, this is a classification of current recorded status.<\/p><div class=\"nds-value-grid\"><div class=\"nds-value\"><h3>Seven categories<\/h3><p>Inspect the complete class-specific confusion matrix.<\/p><\/div><div class=\"nds-value\"><h3>Recorded measurements<\/h3><p>Height and weight help define the classification context.<\/p><\/div><div class=\"nds-value\"><h3>Research scope<\/h3><p>Synthetic records and internal testing limit generalization claims.<\/p><\/div><\/div><div class=\"nds-audience\"><span>Health-data research<\/span><span>Public health<\/span><span>Model evaluation<\/span><\/div><div class=\"nds-note nds-use-boundary\">Research and planning demonstration. It does not establish clinical validity, diagnosis, treatment or donor eligibility.<\/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 UCI dataset contains 2,111 records associated with Colombia, Peru and Mexico. The repository states that 77% were generated synthetically and 23% were collected through a web platform. Height and weight are inputs, so this is classification of recorded status rather than prediction of a future health 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\/544\/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition\">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>1267<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>422<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>422<\/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\">gender<\/th><td>Input<\/td><td>Binary<\/td><td>Female; Male<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>years<\/td><\/tr><tr><th scope=\"row\">height<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>m<\/td><\/tr><tr><th scope=\"row\">weight<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>kg<\/td><\/tr><tr><th scope=\"row\">family_history_with_overweight<\/th><td>Input<\/td><td>Binary<\/td><td>no; yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">caloric_food<\/th><td>Input<\/td><td>Binary<\/td><td>no; yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">vegetables<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">number_meals<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">food_between_meals<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">smoke<\/th><td>Input<\/td><td>Binary<\/td><td>no; yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">water<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">calories<\/th><td>Input<\/td><td>Binary<\/td><td>no; yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">activity<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">technology<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">alcohol<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">transportation<\/th><td>Input<\/td><td>Categorical<\/td><td>automobile; bike; motorbike; public_transportation; walking<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">obesity_level<\/th><td>Target<\/td><td>Categorical<\/td><td>Normal_weight; Obese_I; Obese_II; Obese_III; Overweight_I; Overweight_II; Underweight<\/td><td>As supplied<\/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-obesity-t0-s23-20260921\"><div class=\"nd-chart-host\" id=\"nd-obesity-t0-s23-20260921\" role=\"img\" aria-label=\"obesity_level distribution pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: obesity_level distribution 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\":[\"Normal_weight\",\"Obese_I\",\"Obese_II\",\"Obese_III\",\"Overweight_I\",\"Overweight_II\",\"Underweight\"],\"marker\":{\"colors\":[\"#bfe4f6\",\"#80c8ed\",\"#40ade4\",\"#1d8fc8\",\"#166e9b\",\"#104e6d\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[13.600000381469727,16.600000381469727,14.100000381469727,15.300000190734863,13.699999809265137,13.699999809265137,12.899999618530273]}],\"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\":\"obesity_level distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>obesity_level distribution 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-obesity-t1-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-obesity-t1-s1-20260921\" role=\"img\" aria-label=\"obesity_level Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: obesity_level 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\":[0.11900000274181366,0.13300000131130219,0.14000000059604645,0.15600000321865082,0.19499999284744263,0.20800000429153442,0.22100000083446503,0.2329999953508377,0.2800000011920929,0.5640000104904175],\"y\":[\"number_meals\",\"height\",\"caloric_food\",\"transportation\",\"age\",\"food_between_meals\",\"family_history_with_overweight\",\"gender\",\"vegetables\",\"weight\"]}],\"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\":\"obesity_level 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\":[\"number_meals\",\"height\",\"caloric_food\",\"transportation\",\"age\",\"food_between_meals\",\"family_history_with_overweight\",\"gender\",\"vegetables\",\"weight\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>obesity_level 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\/544\/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition\">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=\"neural_network\" 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>20 encoded input features<\/strong> and <strong>7 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>20<\/td><td>20<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>20<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>7<\/td><td>Softmax<\/td><\/tr><\/tbody><\/table><\/div><p>Output semantics: one score per class in this order: Normal_weight, Obese_I, Obese_II, Obese_III, Overweight_I, Overweight_II, Underweight. The predicted label is the largest score. 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-obesity-t2-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-obesity-t2-s1-20260921.png\" alt=\"Classifying recorded obesity levels: initial Neural Designer architecture\"><\/a><figcaption>Architecture used for this model; no architecture-selection experiment is recorded. Diagram labels show original variables; categorical expansion and the numeric layer dimensions are documented in the model table.<\/figcaption><\/figure><\/section>\n<span id=\"training_strategy\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"4-training\" class=\"nds-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses CrossEntropy with QuasiNewton. 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-obesity-t3-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-obesity-t3-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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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\":[0,184],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,2.0005],\"title\":{\"text\":\"Cross-entropy 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>185<\/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>0.336<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.343<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<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 17.8%. This is a baseline for interpretation, not an alternative model fitted on the test labels.<\/p><\/section>\n<span id=\"testing_analysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"6-clinical-validation\" class=\"nds-card\"><h2>6. Clinical validation<\/h2><p>The final classifier is evaluated on <strong>422 testing records<\/strong>. The confusion counts below were reproduced from the saved model. Rows are actual classes and columns are predicted classes. The multiclass decision is argmax; no binary threshold is applied.<\/p><p>Test class prevalence is shown by the support counts. Accuracy is 87.4% and macro F1 is 0.856. A multiclass ROC AUC was not reported; the confusion matrix and per-class measures are the available evidence.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Actual \/ predicted<\/th><th scope=\"col\">Normal_weight<\/th><th scope=\"col\">Obese_I<\/th><th scope=\"col\">Obese_II<\/th><th scope=\"col\">Obese_III<\/th><th scope=\"col\">Overweight_I<\/th><th scope=\"col\">Overweight_II<\/th><th scope=\"col\">Underweight<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Normal_weight<\/th><td>28<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>3<\/td><td>0<\/td><td>21<\/td><td>52<\/td><\/tr><tr><th scope=\"row\">Obese_I<\/th><td>0<\/td><td>71<\/td><td>3<\/td><td>0<\/td><td>0<\/td><td>1<\/td><td>0<\/td><td>75<\/td><\/tr><tr><th scope=\"row\">Obese_II<\/th><td>0<\/td><td>1<\/td><td>71<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>72<\/td><\/tr><tr><th scope=\"row\">Obese_III<\/th><td>0<\/td><td>0<\/td><td>0<\/td><td>64<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>64<\/td><\/tr><tr><th scope=\"row\">Overweight_I<\/th><td>7<\/td><td>1<\/td><td>0<\/td><td>0<\/td><td>41<\/td><td>7<\/td><td>0<\/td><td>56<\/td><\/tr><tr><th scope=\"row\">Overweight_II<\/th><td>0<\/td><td>1<\/td><td>0<\/td><td>0<\/td><td>7<\/td><td>45<\/td><td>0<\/td><td>53<\/td><\/tr><tr><th scope=\"row\">Underweight<\/th><td>1<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>49<\/td><td>50<\/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\">Normal_weight<\/th><td>52<\/td><td>53.8%<\/td><td>97.8%<\/td><td>77.8%<\/td><td>0.636<\/td><\/tr><tr><th scope=\"row\">Obese_I<\/th><td>75<\/td><td>94.7%<\/td><td>99.1%<\/td><td>95.9%<\/td><td>0.953<\/td><\/tr><tr><th scope=\"row\">Obese_II<\/th><td>72<\/td><td>98.6%<\/td><td>99.1%<\/td><td>95.9%<\/td><td>0.973<\/td><\/tr><tr><th scope=\"row\">Obese_III<\/th><td>64<\/td><td>100.0%<\/td><td>100.0%<\/td><td>100.0%<\/td><td>1<\/td><\/tr><tr><th scope=\"row\">Overweight_I<\/th><td>56<\/td><td>73.2%<\/td><td>97.3%<\/td><td>80.4%<\/td><td>0.766<\/td><\/tr><tr><th scope=\"row\">Overweight_II<\/th><td>53<\/td><td>84.9%<\/td><td>97.8%<\/td><td>84.9%<\/td><td>0.849<\/td><\/tr><tr><th scope=\"row\">Underweight<\/th><td>50<\/td><td>98.0%<\/td><td>94.4%<\/td><td>70.0%<\/td><td>0.817<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-native-test-figures\"><\/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=\"model_deployment\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"7-workflow\" class=\"nds-card\"><h2>7. Workflow and reproducibility<\/h2><p>Validated inputs \u2192 saved preprocessing \u2192 neural network \u2192 score or estimate \u2192 domain review. 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-obesity\"><h3>Explore the exported model<\/h3><p>This research demonstration runs locally in your browser. Values outside the training range are outside the validated domain and are rejected. A valid input range does not guarantee that a combination is physically or operationally plausible.<\/p><p>This is not a diagnosis and must not guide medical treatment or donor eligibility.<\/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 updateSelectedCategory() {\n\tvar selectedCategory = document.getElementById(\"category_select\").value;\n\tvar selectedValueElement = document.getElementById(\"selected_value\");\n\tif(selectedCategory === \"Normal_weight\") {\n\t\tselectedValueElement.value = document.getElementById(\"Normal_weight\").value;\n\t}\n\tif(selectedCategory === \"Obese_I\") {\n\t\tselectedValueElement.value = document.getElementById(\"Obese_I\").value;\n\t}\n\tif(selectedCategory === \"Obese_II\") {\n\t\tselectedValueElement.value = document.getElementById(\"Obese_II\").value;\n\t}\n\tif(selectedCategory === \"Obese_III\") {\n\t\tselectedValueElement.value = document.getElementById(\"Obese_III\").value;\n\t}\n\tif(selectedCategory === \"Overweight_I\") {\n\t\tselectedValueElement.value = document.getElementById(\"Overweight_I\").value;\n\t}\n\tif(selectedCategory === \"Overweight_II\") {\n\t\tselectedValueElement.value = document.getElementById(\"Overweight_II\").value;\n\t}\n\tif(selectedCategory === \"Underweight\") {\n\t\tselectedValueElement.value = document.getElementById(\"Underweight\").value;\n\t}\n}\nfunction Identity(x) {\n\treturn x;\n}\nfunction Tanh(x) {\n\treturn Math.tanh(x);\n}\nfunction Softmax(x) {\n\treturn x;\n}\nfunction calculate_outputs(inputs)\n{\n\tvar gender = +inputs[0];\n\tvar age = +inputs[1];\n\tvar height = +inputs[2];\n\tvar weight = +inputs[3];\n\tvar family_history_with_overweight = +inputs[4];\n\tvar caloric_food = +inputs[5];\n\tvar vegetables = +inputs[6];\n\tvar number_meals = +inputs[7];\n\tvar food_between_meals = +inputs[8];\n\tvar smoke = +inputs[9];\n\tvar water = +inputs[10];\n\tvar calories = +inputs[11];\n\tvar activity = +inputs[12];\n\tvar technology = +inputs[13];\n\tvar alcohol = +inputs[14];\n\tvar automobile = +inputs[15];\n\tvar bike = +inputs[16];\n\tvar motorbike = +inputs[17];\n\tvar public_transportation = +inputs[18];\n\tvar walking = +inputs[19];\n\n\tvar scaled_gender = gender*2.0-1.0;\n\tvar scaled_age = age*0.1576180756-3.83210516;\n\tvar scaled_height = height*10.72010136-18.24218178;\n\tvar scaled_weight = weight*0.03818980232-3.306705952;\n\tvar scaled_family_history_with_overweight = family_history_with_overweight*2.0-1.0;\n\tvar scaled_caloric_food = caloric_food*2.0-1.0;\n\tvar scaled_vegetables = vegetables*1.873353839-4.531717777;\n\tvar scaled_number_meals = number_meals*1.285576701-3.452583551;\n\tvar scaled_food_between_meals = food_between_meals*2.134740591-4.56981802;\n\tvar scaled_smoke = smoke*2.0-1.0;\n\tvar scaled_water = water*1.63183248-3.276735783;\n\tvar scaled_calories = calories*2.0-1.0;\n\tvar scaled_activity = activity*1.175926924-1.188039064;\n\tvar scaled_technology = technology*1.642616153-1.080619693;\n\tvar scaled_alcohol = alcohol*1.940334678-3.359514952;\n\tvar scaled_automobile = automobile*2.0-1.0;\n\tvar scaled_bike = bike*2.0-1.0;\n\tvar scaled_motorbike = motorbike*2.0-1.0;\n\tvar scaled_public_transportation = public_transportation*2.0-1.0;\n\tvar scaled_walking = walking*2.0-1.0;\n\tvar dense_layer_1_output_0 = Tanh( 0.323459655 + (1.447453022*scaled_gender) + (1.270166278*scaled_age) + (-0.2470425069*scaled_height) + (1.013323665*scaled_weight) + (1.107129931*scaled_family_history_with_overweight) + (-1.929280281*scaled_caloric_food) + (-0.2152776122*scaled_vegetables) + (-0.5413691401*scaled_number_meals) + (0.4024477601*scaled_food_between_meals) + (-0.3004409671*scaled_smoke) + (-0.1900756061*scaled_water) + (0.3483017683*scaled_calories) + (0.4025959671*scaled_activity) + (0.2924578488*scaled_technology) + (-0.02578967065*scaled_alcohol) + (-0.5164672732*scaled_automobile) + (-0.2867763937*scaled_bike) + (-0.09778305888*scaled_motorbike) + (-0.5668928623*scaled_public_transportation) + (0.4744282663*scaled_walking) );\n\tvar dense_layer_1_output_1 = Tanh( 0.4854774177 + (0.006585755851*scaled_gender) + (-0.1566550732*scaled_age) + (1.010885954*scaled_height) + (-3.026516676*scaled_weight) + (-0.1746242046*scaled_family_history_with_overweight) + (-0.06515467912*scaled_caloric_food) + (0.03394242004*scaled_vegetables) + (-0.02526909858*scaled_number_meals) + (0.06532901525*scaled_food_between_meals) + (-0.118781358*scaled_smoke) + (0.03365498781*scaled_water) + (-0.04980678484*scaled_calories) + (-0.03197456151*scaled_activity) + (0.06881706417*scaled_technology) + (0.02321674861*scaled_alcohol) + (-0.208606109*scaled_automobile) + (-0.495675385*scaled_bike) + (-0.2457721531*scaled_motorbike) + (-0.2776329815*scaled_public_transportation) + (-0.2479997575*scaled_walking) );\n\tvar dense_layer_1_output_2 = Tanh( -0.3522620499 + (0.1329540461*scaled_gender) + (0.02125914022*scaled_age) + (0.805274725*scaled_height) + (-2.744955301*scaled_weight) + (0.02056225017*scaled_family_history_with_overweight) + (-0.1157566905*scaled_caloric_food) + (0.003193346784*scaled_vegetables) + (-0.02952145226*scaled_number_meals) + (0.04039043561*scaled_food_between_meals) + (0.01816645265*scaled_smoke) + (0.0001605018042*scaled_water) + (-0.02969754674*scaled_calories) + (0.001206669491*scaled_activity) + (-0.003660685616*scaled_technology) + (0.001627683523*scaled_alcohol) + (0.1790022254*scaled_automobile) + (0.1486510485*scaled_bike) + (0.2493285835*scaled_motorbike) + (0.1504632682*scaled_public_transportation) + (0.2859050632*scaled_walking) );\n\tvar Normal_weight = Softmax( 1.101344824 + (-0.2557318211*dense_layer_1_output_0) + (1.374382257*dense_layer_1_output_1) + (5.030637741*dense_layer_1_output_2) );\n\tvar Obese_I = Softmax( -2.007947922 + (0.5902878046*dense_layer_1_output_0) + (1.165216804*dense_layer_1_output_1) + (-7.7905159*dense_layer_1_output_2) );\n\tvar Obese_II = Softmax( 0.001927990466 + (3.063398361*dense_layer_1_output_0) + (-4.464806557*dense_layer_1_output_1) + (-1.748926878*dense_layer_1_output_2) );\n\tvar Obese_III = Softmax( 0.9650056958 + (-2.104794741*dense_layer_1_output_0) + (-3.795145988*dense_layer_1_output_1) + (-1.547477365*dense_layer_1_output_2) );\n\tvar Overweight_I = Softmax( 1.58404541 + (-0.4917184114*dense_layer_1_output_0) + (2.370869875*dense_layer_1_output_1) + (0.6462109685*dense_layer_1_output_2) );\n\tvar Overweight_II = Softmax( -0.698984623 + (1.314927101*dense_layer_1_output_0) + (4.058251858*dense_layer_1_output_1) + (-2.833512306*dense_layer_1_output_2) );\n\tvar Underweight = Softmax( -0.9454137683 + (-2.119925737*dense_layer_1_output_0) + (-0.6875528097*dense_layer_1_output_1) + (8.257764816*dense_layer_1_output_2) );\n\tvar out = [];\n\tout.push(Normal_weight);\n\tout.push(Obese_I);\n\tout.push(Obese_II);\n\tout.push(Obese_III);\n\tout.push(Overweight_I);\n\tout.push(Overweight_II);\n\tout.push(Underweight);\n\n\t\/\/ Softmax (numerically stable)\n\tvar max_out = out[0];\n\tfor(var i = 1; i < out.length; ++i) if(out[i] > max_out) max_out = out[i];\n\tvar sum = 0;\n\tfor(var i = 0; i < out.length; ++i) { out[i] = Math.exp(out[i] - max_out); sum += out[i]; }\n\tfor(var i = 0; i < out.length; ++i) out[i] \/= sum;\n\n\treturn out;\n}\nconst cfg={\"fields\":[{\"name\":\"gender\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"Female\",\"Male\"],\"min\":0.0,\"max\":1.0,\"value\":1,\"unit\":\"\"},{\"name\":\"age\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":14.0,\"max\":61.0,\"value\":22.0,\"unit\":\"years\"},{\"name\":\"height\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.45,\"max\":1.98,\"value\":1.7799999713897705,\"unit\":\"m\"},{\"name\":\"weight\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":39.0,\"max\":173.0,\"value\":89.80000305175781,\"unit\":\"kg\"},{\"name\":\"family_history_with_overweight\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"no\",\"yes\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"caloric_food\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"no\",\"yes\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"vegetables\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":3.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"number_meals\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":1.0,\"unit\":\"\"},{\"name\":\"food_between_meals\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"smoke\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"no\",\"yes\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"water\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":3.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"calories\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"no\",\"yes\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"activity\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":3.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"technology\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":2.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"alcohol\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"transportation\",\"n\":5,\"type\":\"Categorical\",\"categories\":[\"automobile\",\"bike\",\"motorbike\",\"public_transportation\",\"walking\"],\"min\":0.0,\"max\":1.0,\"value\":3,\"unit\":\"\"}],\"output_names\":[\"Normal_weight\",\"Obese_I\",\"Obese_II\",\"Obese_III\",\"Overweight_I\",\"Overweight_II\",\"Underweight\"],\"reference\":[0.003142867934595438,0.05305549821282436,0.0014520015239322737,4.2843239700566866e-05,0.06612955057152309,0.8761749684274066,2.2700900177944527e-06]},root=document.getElementById(\"nd-model-obesity\");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\/obesity-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\/obesity-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\/obesity-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>Synthetic records and related anthropometric predictors limit interpretation of the internal split. Compare with a transparent BMI-based classification, separate original from synthetic subjects, and obtain external validation. Scores are not calibrated probabilities and must not guide individual diagnosis or treatment; clinician or specialist review is required for medical use.<\/p><p>No external validation or independent calibration study is included. 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>Confidence intervals, 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:\/\/archive.ics.uci.edu\/dataset\/544\/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition\">Dataset source and provenance<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/obesity-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 host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const 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