{"id":3488,"date":"2025-09-02T11:12:58","date_gmt":"2025-09-02T09:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/examples-dermatology\/"},"modified":"2026-09-18T16:07:42","modified_gmt":"2026-09-18T14:07:42","slug":"examples-dermatology","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/examples-dermatology\/","title":{"rendered":"Erythemato-squamous disease classification with machine learning"},"content":{"rendered":"<style data-nd-chart-migration=\"1\">\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle 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14px}.nds-kpis,.nds-figure-grid{grid-template-columns:1fr}.nds-executive{padding:24px 20px}.nds-card table{font-size:13px}.nds-card th,.nds-card td{padding:9px 8px}}\n<\/style>\n<style>\n.nds-centered-figure{width:min(860px,100%);margin:24px auto;text-align:center}.nds-centered-figure img{max-width:100%;margin:0 auto 12px}.nds-compact-figure{width:min(660px,100%)}\n.nds-confusion td,.nds-confusion th{text-align:center}.nds-confusion tbody th{text-align:left}.nds-metrics td:nth-child(n+2){text-align:center}\n.nds-flow{display:grid;grid-template-columns:repeat(6,minmax(0,1fr));gap:10px;margin:24px 0}.nds-flow div{display:flex;min-height:100px;align-items:center;justify-content:center;padding:13px 9px;border-radius:13px;background:#12354b;color:#fff;text-align:center;font-weight:700}\n@media(max-width:1000px){.nds-flow{grid-template-columns:repeat(3,1fr)}}@media(max-width:680px){.nds-flow{grid-template-columns:1fr}}\n<\/style>\n<div class=\"nds\" data-health-profile=\"diagnosis\">\n<div class=\"nds-wrap\">\n<section class=\"nds-executive\">\n<h2>Audit a compact six-class dermatology benchmark without unnecessary architecture search<\/h2>\n<p>This reproducible Neural Designer example classifies six erythemato-squamous disease labels from 11 clinical, 22 histopathological and one age variable in the 366-record UCI Dermatology data set. The fixed 34-to-6 softmax model classifies 70 of 73 internal test records correctly. All three errors are pityriasis rosea records assigned to seborrheic dermatitis, and no external or prospective validation is available. The artifact is for research education and model audit, not patient diagnosis.<\/p>\n<div class=\"nds-kpis\">\n<div class=\"nds-kpi\"><strong>366<\/strong><span>historical public records<\/span><\/div>\n<div class=\"nds-kpi\"><strong>73<\/strong><span>internally held-out test records<\/span><\/div>\n<div class=\"nds-kpi\"><strong>95.0%<\/strong><span>macro sensitivity across six labels<\/span><\/div>\n<div class=\"nds-kpi\"><strong>70.0%<\/strong><span>pityriasis rosea sensitivity (7\/10)<\/span><\/div>\n<\/div>\n<div class=\"nds-actions\"><a href=\"#6-clinical-validation\">Review class-level evidence<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-erythemato-squamous-data-2026.csv\">Review the data<\/a><\/div>\n<\/section>\n<ul class=\"nds-toc\">\n<li><a href=\"#1-intended-use\">Question and use<\/a><\/li>\n<li><a href=\"#2-cohort-endpoint\">Cohort and endpoint<\/a><\/li>\n<li><a href=\"#3-model\">Model<\/a><\/li>\n<li><a href=\"#4-training\">Training<\/a><\/li>\n<li><a href=\"#5-selection\">Selection<\/a><\/li>\n<li><a href=\"#6-clinical-validation\">Validation<\/a><\/li>\n<li><a href=\"#7-workflow\">Workflow<\/a><\/li>\n<li><a href=\"#8-safety\">Safety and validity<\/a><\/li>\n<\/ul>\n<section id=\"1-intended-use\" class=\"nds-card\">\n<h2>1. Clinical question and intended use<\/h2>\n<p>The defensible purpose of this artifact is retrospective machine-learning education, software verification and critical appraisal of a classic dermatology benchmark. It reproduces six historical source labels from already collected clinical assessments and biopsy-derived microscopy scores.<\/p>\n<p>The intended users are dermatology researchers, dermatopathology teams, clinical data scientists, biostatisticians and model-governance professionals. The supported action is to inspect data provenance, preprocessing, model parsimony and class-level internal test behaviour before designing a clinically valid study.<\/p>\n<div class=\"nds-value-grid\">\n<div class=\"nds-value\">\n<h3>Prefer parsimony<\/h3>\n<p>Audit the direct 34-to-6 classifier before adding hidden neurons to an already separable benchmark.<\/p>\n<\/div>\n<div class=\"nds-value\">\n<h3>Localize errors<\/h3>\n<p>Move beyond aggregate accuracy and identify the pityriasis rosea\u2013seborrheic dermatitis confusion.<\/p>\n<\/div>\n<div class=\"nds-value\">\n<h3>Define the evidence gap<\/h3>\n<p>Separate internal random-split performance from calibration, external validity and clinical utility.<\/p>\n<\/div>\n<\/div>\n<div class=\"nds-audience\"><span>Dermatology research<\/span><span>Dermatopathology<\/span><span>Clinical data science<\/span><span>Biostatistics<\/span><span>Model governance<\/span><\/div>\n<div class=\"nds-note nds-use-boundary\"><strong>Intended-use boundary.<\/strong> Do not use this model to diagnose, exclude, triage or treat a skin disorder. It requires biopsy-derived histopathological inputs and has not been validated as a medical device or evaluated in a clinical workflow.<\/div>\n<\/section>\n<section id=\"2-cohort-endpoint\" class=\"nds-card\">\n<h2>2. Cohort, measurements and endpoint<\/h2>\n<p>The analysis uses the <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/33\/dermatology\">UCI Dermatology data set<\/a> (DOI <a href=\"https:\/\/doi.org\/10.24432\/C5FK5P\">10.24432\/C5FK5P<\/a>), accessed 13 August 2026 and distributed under CC BY 4.0. UCI reports 366 records and 34 features. Patients were first assessed clinically and skin samples were subsequently evaluated microscopically for histopathological features.<\/p>\n<p>The processed <a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-erythemato-squamous-data-2026.csv\">CSV used here<\/a> contains 11 clinical variables, 22 histopathological variables, age and the six-class endpoint. Except for binary family history and linear age, UCI encodes feature degree from 0 (absent) to 3 (largest amount). These are ordinal assessments, not continuous physical measurements.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Source class<\/th>\n<th>Machine label<\/th>\n<th>Records<\/th>\n<th>Share<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Chronic dermatitis<\/th>\n<td><code>cronic_dermatitis<\/code><\/td>\n<td>52<\/td>\n<td>14.2%<\/td>\n<\/tr>\n<tr>\n<th>Lichen planus<\/th>\n<td><code>lichen_planus<\/code><\/td>\n<td>72<\/td>\n<td>19.7%<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rubra pilaris<\/th>\n<td><code>pitiriasis_rubra_pilaris<\/code><\/td>\n<td>20<\/td>\n<td>5.5%<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rosea<\/th>\n<td><code>pityriasis_rosea<\/code><\/td>\n<td>49<\/td>\n<td>13.4%<\/td>\n<\/tr>\n<tr>\n<th>Psoriasis<\/th>\n<td><code>psoriasis<\/code><\/td>\n<td>112<\/td>\n<td>30.6%<\/td>\n<\/tr>\n<tr>\n<th>Seborrheic dermatitis<\/th>\n<td><code>seboreic_dermatitis<\/code><\/td>\n<td>61<\/td>\n<td>16.7%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The machine-readable labels preserve spelling in the supplied project for exact reproducibility; standard clinical spelling is used in the narrative.<\/p>\n<figure class=\"nds-centered-figure nds-compact-figure\">\n<div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-3488-00\">\n<div class=\"nd-chart-host\" id=\"nd-3488-00\" role=\"img\" aria-label=\"Interactive chart: Distribution of 366 UCI Dermatology records across six source labels\"><\/div>\n<p><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-class-distribution-2026.png\" alt=\"Distribution of 366 UCI Dermatology records across six source labels\"><script type=\"application\/json\" id=\"nd-3488-00-data\">{\"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\":[\"cronic_dermatitis\",\"lichen_planus\",\"pitiriasis_rubra_pilaris\",\"pityriasis_rosea\",\"psoriasis\",\"seboreic_dermatitis\"],\"marker\":{\"colors\":[\"#b5dff4\",\"#6abfea\",\"#209fdf\",\"#1879aa\",\"#115375\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[14.199999809265137,19.700000762939453,5.5,13.399999618530273,30.600000381469727,16.700000762939453]}],\"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\":\"diagnose distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>Sample composition is not disease prevalence. Psoriasis is the largest source class (112 records); pityriasis rubra pilaris is the smallest (20).<\/figcaption><\/figure>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Subset<\/th>\n<th>Rows<\/th>\n<th>Chronic<\/th>\n<th>Lichen<\/th>\n<th>PRP<\/th>\n<th>Pityriasis rosea<\/th>\n<th>Psoriasis<\/th>\n<th>Seborrheic<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Training<\/th>\n<td>220<\/td>\n<td>31<\/td>\n<td>39<\/td>\n<td>11<\/td>\n<td>33<\/td>\n<td>69<\/td>\n<td>37<\/td>\n<\/tr>\n<tr>\n<th>Selection<\/th>\n<td>73<\/td>\n<td>7<\/td>\n<td>19<\/td>\n<td>6<\/td>\n<td>6<\/td>\n<td>27<\/td>\n<td>8<\/td>\n<\/tr>\n<tr>\n<th>Testing<\/th>\n<td>73<\/td>\n<td>14<\/td>\n<td>14<\/td>\n<td>3<\/td>\n<td>10<\/td>\n<td>16<\/td>\n<td>16<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The stored project uses a random 60\/20\/20 row split. The audit found no duplicate complete rows or duplicate input vectors. Eight records have missing age; the project applies mean replacement.<\/p>\n<div class=\"nds-note nds-note--warning\"><strong>Preprocessing leakage.<\/strong> The exported age scaler is centred at 36.2961 years, exactly the mean of all 358 observed ages, rather than the 35.5185-year mean in the training subset. The test partition therefore influenced preprocessing and is not a fully untouched estimate. A corrected study should fit imputation and scaling on training data only.<\/div>\n<figure class=\"nds-centered-figure\">\n<div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-3488-01\">\n<div class=\"nd-chart-host\" id=\"nd-3488-01\" role=\"img\" aria-label=\"Interactive chart: Ten largest univariate associations between dermatology inputs and the six-class source label\"><\/div>\n<p><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-input-target-associations-2026.png\" alt=\"Ten largest univariate associations between dermatology inputs and the six-class source label\"><script type=\"application\/json\" id=\"nd-3488-01-data\">{\"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\":[0.5080000162124634,0.515999972820282,0.5180000066757202,0.5199999809265137,0.5199999809265137,0.5199999809265137,0.5210000276565552,0.5239999890327454,0.5709999799728394,0.574999988079071],\"y\":[\"oral_mucosal_involvement\",\"polygonal_papules\",\"focal_hypergranulosis\",\"melanin_incontinence\",\"vacuolisation_and_damage_of_basal_layer\",\"band_like_infiltrate\",\"saw_tooth_appearance_of_retes\",\"elongation_of_the_rete_ridges\",\"clubbing_of_the_rete_ridges\",\"thinning_of_the_suprapapillary_epidermis\"]}],\"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\":\"diagnose 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\":[\"oral_mucosal_involvement\",\"polygonal_papules\",\"focal_hypergranulosis\",\"melanin_incontinence\",\"vacuolisation_and_damage_of_basal_layer\",\"band_like_infiltrate\",\"saw_tooth_appearance_of_retes\",\"elongation_of_the_rete_ridges\",\"clubbing_of_the_rete_ridges\",\"thinning_of_the_suprapapillary_epidermis\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>These label-coding- and method-dependent univariate associations are not feature importance, causal effects or evidence that a finding is independently diagnostic.<\/figcaption><\/figure>\n<div class=\"nds-note nds-note--provenance\"><strong>Endpoint provenance.<\/strong> The accompanying paper describes records with known diagnoses, but the public repository does not provide the individual reference-standard procedure, reader count, blinding, adjudication or recruitment setting for each record. The six labels must therefore be treated as historical benchmark annotations, not a fully characterized contemporary clinical ground truth.<\/div>\n<\/section>\n<section id=\"3-model\" class=\"nds-card\">\n<h2>3. Model<\/h2>\n<p>All 34 inputs are scaled and connected directly to six softmax outputs. There is no hidden layer, so the model is a multinomial logistic classifier with 210 trainable parameters: 204 input weights and six biases.<\/p>\n<p>This compact structure is appropriate to audit first: the data set is small, the inputs are expert-scored and the classes are already strongly separable. Additional hidden neurons would increase flexibility and selection burden without evidence that the base model needs it.<\/p>\n<div class=\"nds-note\"><strong>Output contract.<\/strong> The six exported softmax values sum to one but were not calibrated. They are model scores for the source labels, not patient-level diagnostic probabilities or measures of clinical certainty.<\/div>\n<figure class=\"nds-architecture-figure\" data-model-stage=\"initial\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-network-architecture-2026.png\" alt=\"Direct 34-input to six-output dermatology classifier without a hidden layer\"><figcaption>Initial and final architecture: 34 scaled inputs connect directly to six softmax scores. The diagram summarizes the six scores under the endpoint node and omits middle inputs for readability.<\/figcaption><\/figure>\n<\/section>\n<section id=\"4-training\" class=\"nds-card\">\n<h2>4. Training strategy<\/h2>\n<p>The stored project minimizes multiclass cross-entropy with L2 regularization weight 0.01 using the quasi-Newton optimizer. Training ends on minimum loss decrease after 35 stored epochs (0\u201334).<\/p>\n<p>Training cross-entropy falls from 2.0043 to 0.0544. Selection cross-entropy falls from 0.2776 to its minimum of 0.0743 at epoch 7 and ends at 0.0784. The modest post-minimum increase is an overfitting signal; the exported final state is not documented as a restored best-selection checkpoint.<\/p>\n<figure class=\"nds-centered-figure\">\n<div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-3488-03\">\n<div class=\"nd-chart-host\" id=\"nd-3488-03\" role=\"img\" aria-label=\"Interactive chart: Training and selection cross-entropy over 35 quasi-Newton epochs\"><\/div>\n<p><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-training-history-2026.png\" alt=\"Training and selection cross-entropy over 35 quasi-Newton epochs\"><script type=\"application\/json\" id=\"nd-3488-03-data\">{\"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 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],\"y\":[2,0.36500000953674316,0.24500000476837158,0.14300000667572021,0.10400000214576721,0.08110000193119049,0.06909999996423721,0.062199998646974564,0.056299999356269836,0.05130000039935112,0.05050000175833702,0.053599998354911804,0.05609999969601631,0.05810000002384186,0.058800000697374344,0.05770000070333481,0.055399999022483826,0.05400000140070915,0.05339999869465828,0.05290000140666962,0.05290000140666962,0.053599998354911804,0.0544000007212162,0.05469999834895134,0.05469999834895134,0.0544000007212162,0.054099999368190765,0.05389999970793724,0.05400000140070915,0.05420000106096268,0.0544000007212162,0.054499998688697815,0.05460000038146973,0.05460000038146973,0.0544000007212162]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Selection 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],\"y\":[0.27799999713897705,0.19599999487400055,0.12399999797344208,0.09740000218153,0.08320000022649765,0.07760000228881836,0.07530000060796738,0.07429999858140945,0.0746999979019165,0.07689999788999557,0.07909999787807465,0.07999999821186066,0.0803999975323677,0.08049999922513962,0.0803999975323677,0.07999999821186066,0.07970000058412552,0.0794999971985817,0.07919999957084656,0.07880000025033951,0.07850000262260437,0.07829999923706055,0.07829999923706055,0.07829999923706055,0.07840000092983246,0.07859999686479568,0.07880000025033951,0.07900000363588333,0.07900000363588333,0.07900000363588333,0.07890000194311142,0.0786999985575676,0.07850000262260437,0.07840000092983246,0.07840000092983246]}],\"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\":[0,34],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,2.0005],\"title\":{\"text\":\"CrossEntropy\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>The selection curve reaches its minimum at epoch 7 while training continues. This supports checkpointing the best selection state in a future leakage-free experiment.<\/figcaption><\/figure>\n<\/section>\n<section id=\"5-selection\" class=\"nds-card\">\n<h2>5. Model selection and baseline<\/h2>\n<p><strong>No input selection, neuron selection or architecture search was performed.<\/strong> The project contains default configuration panels for growing inputs and growing neurons, but no corresponding selection task or result. The direct 34-to-6 classifier is both the initial and final model.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Testing reference<\/th>\n<th>Accuracy<\/th>\n<th>Macro sensitivity<\/th>\n<th>Model complexity<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Always predict psoriasis<\/th>\n<td>21.9% (16\/73)<\/td>\n<td>16.7%<\/td>\n<td>No fitted parameters<\/td>\n<\/tr>\n<tr>\n<th>Fixed direct softmax model<\/th>\n<td>95.9% (70\/73)<\/td>\n<td>95.0%<\/td>\n<td>210 parameters<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The base model substantially exceeds the transparent majority-class baseline. Because the result is already strong on this internal split, the next useful experiment is not a wider network: it is a leakage-free, repeated or nested evaluation followed by independent external validation.<\/p>\n<\/section>\n<section id=\"6-clinical-validation\" class=\"nds-card\">\n<h2>6. Clinical validation<\/h2>\n<p>The exact Python export was recomputed against the 73 records marked as testing in the project after applying the project-wide age mean. This reproduces the supplied confusion table: 70 correct and three incorrect classifications.<\/p>\n<p>Calibration, multiclass ROC AUC and PR AUC were not evaluated. Class counts range from only three pityriasis rubra pilaris records to 16 psoriasis and 16 seborrheic dermatitis records, so apparently perfect class sensitivities remain uncertain.<\/p>\n<h3>Decision rule and confusion matrix<\/h3>\n<p>No binary or class-specific threshold was selected. The reported operating point assigns the label with the largest of the six softmax scores (<code>argmax<\/code>); no threshold was optimized on the test subset.<\/p>\n<div class=\"nds-table-scroll\">\n<table class=\"nds-confusion\">\n<thead>\n<tr>\n<th>Actual \/ predicted<\/th>\n<th>Chronic<\/th>\n<th>Lichen<\/th>\n<th>PRP<\/th>\n<th>Pityriasis rosea<\/th>\n<th>Psoriasis<\/th>\n<th>Seborrheic<\/th>\n<th>Total<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Chronic dermatitis<\/th>\n<td>14<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>14<\/td>\n<\/tr>\n<tr>\n<th>Lichen planus<\/th>\n<td>0<\/td>\n<td>14<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>14<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rubra pilaris<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>3<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rosea<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>7<\/td>\n<td>0<\/td>\n<td>3<\/td>\n<td>10<\/td>\n<\/tr>\n<tr>\n<th>Psoriasis<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>16<\/td>\n<td>0<\/td>\n<td>16<\/td>\n<\/tr>\n<tr>\n<th>Seborrheic dermatitis<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>16<\/td>\n<td>16<\/td>\n<\/tr>\n<tr>\n<th>Total<\/th>\n<td>14<\/td>\n<td>14<\/td>\n<td>3<\/td>\n<td>7<\/td>\n<td>16<\/td>\n<td>19<\/td>\n<td>73<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-table-scroll\">\n<table class=\"nds-metrics\">\n<thead>\n<tr>\n<th>One-vs-rest label<\/th>\n<th>Test prevalence<\/th>\n<th>TP \/ FN \/ FP \/ TN<\/th>\n<th>Sensitivity (95% Wilson CI)<\/th>\n<th>Specificity<\/th>\n<th>Observed precision \/ PPV<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Chronic dermatitis<\/th>\n<td>14\/73 (19.2%)<\/td>\n<td>14 \/ 0 \/ 0 \/ 59<\/td>\n<td>100% (78.5\u2013100)<\/td>\n<td>100%<\/td>\n<td>100%<\/td>\n<\/tr>\n<tr>\n<th>Lichen planus<\/th>\n<td>14\/73 (19.2%)<\/td>\n<td>14 \/ 0 \/ 0 \/ 59<\/td>\n<td>100% (78.5\u2013100)<\/td>\n<td>100%<\/td>\n<td>100%<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rubra pilaris<\/th>\n<td>3\/73 (4.1%)<\/td>\n<td>3 \/ 0 \/ 0 \/ 70<\/td>\n<td>100% (43.9\u2013100)<\/td>\n<td>100%<\/td>\n<td>100%<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rosea<\/th>\n<td>10\/73 (13.7%)<\/td>\n<td>7 \/ 3 \/ 0 \/ 63<\/td>\n<td>70.0% (39.7\u201389.2)<\/td>\n<td>100%<\/td>\n<td>100%<\/td>\n<\/tr>\n<tr>\n<th>Psoriasis<\/th>\n<td>16\/73 (21.9%)<\/td>\n<td>16 \/ 0 \/ 0 \/ 57<\/td>\n<td>100% (80.6\u2013100)<\/td>\n<td>100%<\/td>\n<td>100%<\/td>\n<\/tr>\n<tr>\n<th>Seborrheic dermatitis<\/th>\n<td>16\/73 (21.9%)<\/td>\n<td>16 \/ 0 \/ 3 \/ 54<\/td>\n<td>100% (80.6\u2013100)<\/td>\n<td>94.7%<\/td>\n<td>84.2%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Overall test summary<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Accuracy<\/th>\n<td>95.9% (70\/73)<\/td>\n<\/tr>\n<tr>\n<th>Macro sensitivity \/ balanced multiclass recall<\/th>\n<td>95.0%<\/td>\n<\/tr>\n<tr>\n<th>Macro precision<\/th>\n<td>97.4%<\/td>\n<\/tr>\n<tr>\n<th>Macro F1<\/th>\n<td>0.956<\/td>\n<\/tr>\n<tr>\n<th>Majority-class baseline accuracy<\/th>\n<td>21.9% (16\/73)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-figure-grid\"><\/div>\n<div class=\"nds-note\"><strong>Interpretation.<\/strong> On this split, errors are not diffuse: three of ten pityriasis rosea records are assigned to seborrheic dermatitis. The 100% observed sensitivities for other classes are based on 3\u201316 records each and have wide confidence intervals. Predictive values reflect this benchmark composition and must not be transferred to a clinical population.<\/div>\n<\/section>\n<section id=\"7-workflow\" class=\"nds-card\">\n<h2>7. Workflow and reproducibility<\/h2>\n<p>A responsible workflow for this artifact is a controlled research reproduction:<\/p>\n<div class=\"nds-flow\">\n<div>Licensed de-identified record<\/div>\n<div>Schema and ordinal-scale check<\/div>\n<div>Training-only preprocessing<\/div>\n<div>Six uncalibrated scores<\/div>\n<div>Error and uncertainty audit<\/div>\n<div>Researcher review<\/div>\n<\/div>\n<p>Use batch inference only with the supplied schema. A valid record requires all 34 ordered inputs, including 22 biopsy-derived histopathological assessments. Reject unknown encodings, out-of-range ordinal values and missing fields outside the documented age-imputation reproduction path.<\/p>\n<p>No interactive patient calculator is provided: manual entry of 34 expert-scored findings would invite transcription errors and imply a clinical use that this evidence does not support.<\/p>\n<h3>Verified reference calculation<\/h3>\n<p>The exact export was checked with this ordered 34-value vector:<\/p>\n<p><code>[2, 1, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 36]<\/code><\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Source label<\/th>\n<th>Model score<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Chronic dermatitis<\/th>\n<td>0.967870<\/td>\n<\/tr>\n<tr>\n<th>Lichen planus<\/th>\n<td>0.002199<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rubra pilaris<\/th>\n<td>0.001761<\/td>\n<\/tr>\n<tr>\n<th>Pityriasis rosea<\/th>\n<td>0.003867<\/td>\n<\/tr>\n<tr>\n<th>Psoriasis<\/th>\n<td>0.006463<\/td>\n<\/tr>\n<tr>\n<th>Seborrheic dermatitis<\/th>\n<td>0.017840<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The Python package contains the exact export, ordered input schema, reference vector and expected scores. The Neural Designer package contains the project and exact processed CSV, preserving the stored split, base model and regenerated analyses.<\/p>\n<div class=\"nds-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-erythemato-squamous-data-2026.csv\">Download data<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-neural-designer-project-2026.zip\">Neural Designer project<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/dermatology-python-model-2026.zip\">Python model<\/a><\/div>\n<\/section>\n<section id=\"8-safety\" class=\"nds-card\">\n<h2>8. Safety, generalizability and governance<\/h2>\n<ul>\n<li><strong>Internal evidence only:<\/strong> there is no independent-site, temporal or prospective validation and no workflow-impact study.<\/li>\n<li><strong>Preprocessing leakage:<\/strong> the scaler uses complete-data statistics, so the random test subset is not an untouched estimate.<\/li>\n<li><strong>Historical provenance:<\/strong> the records were donated in 1997; the public metadata does not establish contemporary diagnostic criteria, recruitment, geography, reader agreement or adjudication for every record.<\/li>\n<li><strong>Small class samples:<\/strong> the test set contains only three pityriasis rubra pilaris and ten pityriasis rosea records. Perfect observed results do not imply negligible error rates.<\/li>\n<li><strong>Calibration and thresholds:<\/strong> calibration, multiclass ROC AUC, PR AUC, decision-curve utility and clinically prespecified operating points are absent.<\/li>\n<li><strong>Expert-dependent inputs:<\/strong> 22 variables require microscopy of skin samples and many fields use subjective ordinal grading. Inter-reader and inter-site reproducibility were not evaluated.<\/li>\n<li><strong>Missingness:<\/strong> eight ages are missing and reproduced using a complete-data mean. Deployment missingness mechanisms may differ.<\/li>\n<li><strong>Subgroups and spectrum:<\/strong> performance by age, sex, skin tone, disease stage, treatment status and coexisting conditions is not available.<\/li>\n<li><strong>Human oversight:<\/strong> any future study requires specialist review by dermatology and dermatopathology professionals, locked preprocessing, audit logging, an abstention pathway and confirmatory clinical procedures.<\/li>\n<\/ul>\n<h3>Evidence required before any clinical research transition<\/h3>\n<ol>\n<li>Reconstruct the reference standard, inclusion criteria, acquisition sites and reader process from primary records.<\/li>\n<li>Repeat development with stratification and training-only imputation\/scaling, using nested or repeated resampling for model comparison.<\/li>\n<li>Prespecify clinically meaningful operating points and evaluate calibration, uncertainty and abstention.<\/li>\n<li>Validate externally across sites, time periods, acquisition workflows and relevant demographic and disease subgroups.<\/li>\n<li>Compare against dermatologist and dermatopathologist assessment and evaluate prospective workflow effects before considering regulated use.<\/li>\n<\/ol>\n<div class=\"nds-note nds-note--warning\"><strong>Decision boundary.<\/strong> This article demonstrates reproducibility, parsimony and critical evaluation of a historical benchmark. It does not establish clinical validity, clinical benefit or permission to use the model with patient data.<\/div>\n<\/section>\n<section id=\"references\" class=\"nds-card\">\n<h2>References<\/h2>\n<ol>\n<li>\u0130lter N, G\u00fcvenir H. <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/33\/dermatology\">Dermatology<\/a>. UCI Machine Learning Repository; 1998. DOI: <a href=\"https:\/\/doi.org\/10.24432\/C5FK5P\">10.24432\/C5FK5P<\/a>.<\/li>\n<li>G\u00fcvenir HA, Demir\u00f6z G, \u0130lter N. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/9698151\/\">Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals<\/a>. Artificial Intelligence in Medicine. 1998;13(3):147\u2013165. DOI: <a href=\"https:\/\/doi.org\/10.1016\/S0933-3657(98)00028-1\">10.1016\/S0933-3657(98)00028-1<\/a>.<\/li>\n<\/ol>\n<\/section>\n<\/div>\n<\/div>\n<p><script data-nd-chart-migration=\"1\" data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-nd-chart-migration=\"1\" 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 ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script><\/p>\n","protected":false},"author":13,"featured_media":2286,"template":"","categories":[29],"tags":[38],"class_list":["post-3488","learning","type-learning","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\/ 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