{"id":3473,"date":"2025-08-31T11:12:59","date_gmt":"2025-08-31T09:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/cervical-cancer-prognosis\/"},"modified":"2026-08-12T15:20:33","modified_gmt":"2026-08-12T13:20:33","slug":"cervical-cancer-prognosis","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/cervical-cancer-prognosis\/","title":{"rendered":"Classify high-grade cervical lesions with machine learning"},"content":{"rendered":"<style>\n.nds{--nds-code-bg:#f3f7fa;--nds-code-fg:#12354b;width:100vw;margin-left:calc(50% - 50vw);padding:22px 24px 14px;background:#eee;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.nds *{box-sizing:border-box}.nds-wrap{width:min(100%,1200px);margin:0 auto}.nds a{text-decoration:none;color:#2d799f}\n.nds-executive{margin:0 0 34px;padding:32px;border-radius:20px;background:linear-gradient(135deg,#12354b,#245e80);color:#fff}.nds-executive h2{margin:0 0 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code,.nds .wp-block-code code *{border:0!important;background:transparent!important;color:var(--nds-code-fg)!important;box-shadow:none!important;text-shadow:none!important;font-family:Consolas,\"Liberation Mono\",monospace!important;font-size:14px;line-height:1.55}\n.nds :not(pre)>code{padding:.08em .32em;border-radius:4px;background:#e6eef3!important;color:#12354b!important}.nds .nds-expression{max-width:100%;overflow:auto;margin:22px auto;padding:18px 20px;border:1px solid #d5e2e9;border-radius:12px;background:#fff!important;color:#12354b!important;text-align:center}.nds .nds-expression math,.nds .nds-expression mjx-container,.nds .nds-expression .MathJax{background:transparent!important;color:#12354b!important}\n.nds .nds-calculator-grid>br,.nds .nds-calculator-grid>p:empty{display:none!important}\n@media(max-width:820px){.nds-kpis{grid-template-columns:repeat(2,minmax(0,1fr))}.nds-value-grid{grid-template-columns:1fr}}@media(max-width:620px){.nds{padding:12px 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>.nds-card h3{margin:27px 0 12px;color:#12354b;font-size:20px}.nds-centered-figure{width:min(680px,100%);margin:24px auto;padding:16px;border:1px solid #dce8ef;border-radius:15px;background:#f8fbfd}.nds-centered-figure img,.nds-roc-figure img{width:100%;max-width:100%;margin:0 auto 12px}.nds-roc-figure{width:min(600px,100%);margin:24px auto;padding:16px;border:1px solid #dce8ef;border-radius:15px;background:#f8fbfd}.nds-validation-grid{display:grid;grid-template-columns:1fr 1.15fr;gap:20px;align-items:start}.nds-flow{display:grid;grid-template-columns:repeat(6,minmax(0,1fr));gap:10px;margin:24px 0}.nds-flow div{display:flex;min-height:102px;align-items:center;justify-content:center;padding:13px 8px;border-radius:13px;background:#12354b;color:#fff;text-align:center;font-weight:700}.nds-calculator{margin:28px 0;padding:24px;border:1px solid #cfe1eb;border-radius:18px;background:#f7fbfd}.nds-calculator-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:15px}.nds-calculator-grid>br,.nds-calculator-grid>p:empty{display:none!important}.nds-calculator label{display:flex;min-width:0;flex-direction:column;gap:6px;color:#12354b;font-size:13px;font-weight:700}.nds-calculator input,.nds-calculator select{width:100%;min-height:46px;padding:10px 12px;border:1px solid #b9cfdb;border-radius:9px;background:#fff;color:#173246;font:inherit}.nds-calculator .is-invalid{border-color:#c94747;background:#fff8f8}.nds-calculator-actions{display:flex;flex-wrap:wrap;justify-content:center;gap:10px;margin:20px 0}.nds-calculator button{padding:11px 19px;border:0;border-radius:24px;background:#245e80;color:#fff;font:inherit;font-weight:700;cursor:pointer}.nds-calculator .nds-secondary-action{background:#e1edf3;color:#12354b}.nds-calculator-error{min-height:24px;color:#a12828;text-align:center;font-weight:600}.nds-health-score{max-width:520px;margin:0 auto;padding:18px;border:1px solid #d6e4eb;border-radius:13px;background:#fff;text-align:center}.nds-health-score span,.nds-health-score strong{display:block}.nds-health-score strong{margin:6px 0;color:#12354b;font-size:27px}@media(max-width:1000px){.nds-flow{grid-template-columns:repeat(3,1fr)}.nds-validation-grid{grid-template-columns:1fr}}@media(max-width:760px){.nds-flow,.nds-calculator-grid{grid-template-columns:1fr}}<\/style>\n<div class=\"nds\" data-health-profile=\"diagnosis\">\n<div class=\"nds-wrap\">\n<section class=\"nds-executive\">\n<h2>Classify the source-derived CIN II-or-worse endpoint<\/h2>\n<p>This reproducible Neural Designer example reformulates 197 cervical-pathology records as a binary classification problem instead of assigning arbitrary numbers to lesion grades. On 39 internally held-out records, the selected model reaches ROC AUC 0.756 and 76.0% sensitivity at score 0.50. Limited provenance, possible endpoint leakage and the absence of external validation keep it firmly within retrospective research.<\/p>\n<div class=\"nds-kpis\">\n<div class=\"nds-kpi\"><strong>197<\/strong><span>source records<\/span><\/div>\n<div class=\"nds-kpi\"><strong>39<\/strong><span>internally held-out records<\/span><\/div>\n<div class=\"nds-kpi\"><strong>0.756<\/strong><span>testing ROC AUC (95% CI 0.629\u20130.883)<\/span><\/div>\n<div class=\"nds-kpi\"><strong>76.0%<\/strong><span>testing sensitivity at score 0.50<\/span><\/div>\n<\/div>\n<div class=\"nds-actions\"><a href=\"#6-clinical-validation\">Review the validation<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervixcancer.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 model learns the binary endpoint <code>cin2_or_worse<\/code> from age, cytology, HPV group, biopsy result, p16\/Ki-67 and smoking status. The defensible purpose is to demonstrate categorical preprocessing, class-weighted training, model selection and internal validation on a small historical table.<\/p>\n<p>This is not a longitudinal prognosis model: the source does not document an index date, prediction horizon or later outcome. The revised endpoint describes the record&#8217;s final source label and is therefore presented as retrospective classification.<\/p>\n<div class=\"nds-value-grid\">\n<div class=\"nds-value\">\n<h3>Preserve categories<\/h3>\n<p>Use categorical expansion instead of imposing a questionable numeric distance between cytology, HPV and biopsy labels.<\/p>\n<\/div>\n<div class=\"nds-value\">\n<h3>Audit an operating point<\/h3>\n<p>Report counts, sensitivity and specificity at a fixed score threshold rather than a goodness-of-fit chart for an ordinal regression.<\/p>\n<\/div>\n<div class=\"nds-value\">\n<h3>Expose evidence limits<\/h3>\n<p>Compare the network with transparent baselines and separate reproducibility from clinical validity.<\/p>\n<\/div>\n<\/div>\n<div class=\"nds-audience\"><span>Clinical data science<\/span><span>Cervical pathology research<\/span><span>Medical ML education<\/span><span>Biostatistics<\/span><span>Model governance<\/span><\/div>\n<div class=\"nds-note nds-use-boundary\"><strong>Intended-use boundary.<\/strong> This is a retrospective educational classifier of a source-derived label. It must not determine population screening, triage, diagnosis, follow-up intervals, treatment or discharge from care.<\/div>\n<\/section>\n<section id=\"2-cohort-endpoint\" class=\"nds-card\">\n<h2>2. Cohort, measurements and endpoint<\/h2>\n<p>The updated <a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervixcancer.csv\"><code>cervixcancer.csv<\/code><\/a> contains 197 records. The previous numeric grades have been replaced by explicit categories, and the former continuous target has been derived as <code>cin2_or_worse<\/code>: <code>0<\/code> for negative\/CIN I and <code>1<\/code> for CIN II, CIN II\u2013III, CIN III or carcinoma.<\/p>\n<div class=\"nds-note nds-note--warning\"><strong>Source provenance.<\/strong> The previous article attributes the table to the Cervical Pathology Unit of the Palencia health area in Spain and says it covers three years, but it provides no exact dates, recruitment protocol, reference-standard timing, follow-up horizon, ethics statement or primary data publication. Those missing details prevent a clinical prognosis claim.<\/div>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>CSV field<\/th>\n<th>Role<\/th>\n<th>Values and interpretation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th><code>patient_id<\/code><\/th>\n<td>Identifier<\/td>\n<td>Stable row identifier; excluded from modelling.<\/td>\n<\/tr>\n<tr>\n<th><code>age<\/code><\/th>\n<td>Input<\/td>\n<td>Age in years, 20\u201368.<\/td>\n<\/tr>\n<tr>\n<th><code>cytology<\/code><\/th>\n<td>Input<\/td>\n<td>Normal, ASC-US, ASC-H, LSIL, HSIL, AGC or unknown.<\/td>\n<\/tr>\n<tr>\n<th><code>hpv_risk_group<\/code><\/th>\n<td>Input<\/td>\n<td>Negative, other low risk, other high risk, HPV 16\/18 or unknown.<\/td>\n<\/tr>\n<tr>\n<th><code>biopsy_result<\/code><\/th>\n<td>Input<\/td>\n<td>Negative\/nondiagnostic, CIN I, CIN II, CIN II\u2013III, CIN III, carcinoma or unknown. The source numeric table had already merged negative and nondiagnostic values, so they cannot be separated retrospectively.<\/td>\n<\/tr>\n<tr>\n<th><code>p16_ki67<\/code><\/th>\n<td>Input<\/td>\n<td>Negative, positive or unknown.<\/td>\n<\/tr>\n<tr>\n<th><code>smoking_status<\/code><\/th>\n<td>Input<\/td>\n<td>No, yes or unknown.<\/td>\n<\/tr>\n<tr>\n<th><code>cin2_or_worse<\/code><\/th>\n<td>Target<\/td>\n<td>Binary source-derived endpoint: 85 negative\/CIN I and 112 CIN II-or-worse records.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Missing source values remain explicit <code>unknown<\/code> categories rather than being silently imputed. Before recoding, missingness affected 5 cytology, 2 HPV, 3 biopsy, 78 p16\/Ki-67 and 103 smoking entries. In particular, high missingness may reflect the local testing workflow rather than biology.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Subset<\/th>\n<th>Rows<\/th>\n<th>Endpoint 0<\/th>\n<th>Endpoint 1<\/th>\n<th>Purpose<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Training<\/th>\n<td>119<\/td>\n<td>49<\/td>\n<td>70<\/td>\n<td>Estimate model parameters<\/td>\n<\/tr>\n<tr>\n<th>Selection<\/th>\n<td>39<\/td>\n<td>22<\/td>\n<td>17<\/td>\n<td>Select hidden-layer size<\/td>\n<\/tr>\n<tr>\n<th>Testing<\/th>\n<td>39<\/td>\n<td>14<\/td>\n<td>25<\/td>\n<td>Internal final analysis<\/td>\n<\/tr>\n<tr>\n<th>Total<\/th>\n<td>197<\/td>\n<td>85<\/td>\n<td>112<\/td>\n<td>Random 60\/20\/20 row split<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-figure-grid\">\n<figure><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-class-distribution-2026.png\" alt=\"Distribution of the binary CIN2-or-worse endpoint\"><figcaption>The 56.9% positive share is the class balance of this source table, not population prevalence.<\/figcaption><\/figure>\n<figure><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-input-target-correlations-2026.png\" alt=\"Coding-dependent input-target Pearson correlations\"><figcaption>Biopsy has the largest displayed coefficient. Pearson values for encoded categories are descriptive, coding-dependent associations\u2014not feature importance, causal effects or independent clinical evidence.<\/figcaption><\/figure>\n<\/div>\n<div class=\"nds-note nds-note--provenance\"><strong>Split-integrity audit.<\/strong> The 197 rows collapse to 190 unique input vectors; seven are exact extra duplicates and two testing vectors also appear in training. This row-level leakage can make internal performance optimistic. Future work should group identical records and, preferably, split by time, patient or clinical site.<\/div>\n<\/section>\n<section id=\"3-model\" class=\"nds-card\">\n<h2>3. Model<\/h2>\n<p>Age remains numeric. Neural Designer one-hot expands the five categorical variables, producing 26 model features. The initial architecture connects those 26 scaled features directly to one sigmoid output, so it is a compact linear baseline in the expanded feature space.<\/p>\n<div class=\"nds-note\"><strong>Output contract.<\/strong> A larger sigmoid value ranks a record toward the source-derived <code>cin2_or_worse=1<\/code> label. It has not been calibrated as an individual clinical probability.<\/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\/cervical-cin2plus-initial-architecture-2026.png\" alt=\"Initial 26-to-1 cervical CIN2-plus classifier\"><figcaption>Initial 26\u20131 architecture before neuron selection. Six displayed source inputs expand to 26 numeric model features.<\/figcaption><\/figure>\n<\/section>\n<section id=\"4-training\" class=\"nds-card\">\n<h2>4. Training strategy<\/h2>\n<p>The initial model minimizes weighted squared error with quasi-Newton optimization, L2 regularization 0.01 and class weights 1.1588 for endpoint 0 and 0.8795 for endpoint 1. Weighting prevents the larger class from dominating the loss.<\/p>\n<p>Across 35 stored iterations, training error decreases from 1.0178 to 0.3580 and selection error from 1.1765 to 0.8203. Optimization stops on minimum loss decrease.<\/p>\n<figure class=\"nds-centered-figure\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-initial-training-2026.png\" alt=\"Initial weighted-squared training and selection error histories\"><figcaption>Training and selection are separated before architecture selection; testing data remain reserved for the final analysis.<\/figcaption><\/figure>\n<\/section>\n<section id=\"5-selection\" class=\"nds-card\">\n<h2>5. Model selection and baseline<\/h2>\n<p>A growing-neurons experiment evaluates hidden layers from one to ten neurons with three trials per size. The minimum stored selection error occurs at seven tanh neurons: training error 0.1445 and selection error 0.6884.<\/p>\n<figure class=\"nds-centered-figure\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-neuron-selection-2026.png\" alt=\"Growing-neurons selection from one to ten hidden neurons\"><figcaption>Seven neurons are selected from the selection subset; the testing subset is not used to choose this architecture.<\/figcaption><\/figure>\n<h3>Selected architecture<\/h3>\n<p>The final 26\u20137\u20131 network contains 197 trainable parameters and is the model used for testing and deployment.<\/p>\n<figure class=\"nds-architecture-figure\" data-model-stage=\"selected\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-selected-architecture-2026.png\" alt=\"Selected 26-to-7-to-1 cervical CIN2-plus classifier\"><figcaption>Final 26\u20137\u20131 architecture obtained after neuron selection.<\/figcaption><\/figure>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Testing reference at score 0.50<\/th>\n<th>Sensitivity<\/th>\n<th>Specificity<\/th>\n<th>Accuracy<\/th>\n<th>Balanced accuracy<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Always predict endpoint 1<\/th>\n<td>100.0%<\/td>\n<td>0%<\/td>\n<td>64.1%<\/td>\n<td>50.0%<\/td>\n<\/tr>\n<tr>\n<th>Positive when biopsy is CIN II+<\/th>\n<td>84.0%<\/td>\n<td>64.3%<\/td>\n<td>76.9%<\/td>\n<td>74.1%<\/td>\n<\/tr>\n<tr>\n<th>Selected neural network<\/th>\n<td>76.0%<\/td>\n<td>64.3%<\/td>\n<td>71.8%<\/td>\n<td>70.1%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The transparent biopsy rule slightly outperforms the selected network on this small test subset. The network therefore demonstrates a reproducible modelling workflow, but it does not establish incremental clinical value.<\/p>\n<\/section>\n<section id=\"6-clinical-validation\" class=\"nds-card\">\n<h2>6. Clinical validation<\/h2>\n<p>The selected model is evaluated on 39 held-out records containing 25 positive and 14 negative source labels. Neural Designer reports ROC AUC 0.756 with a 95% confidence interval of 0.629\u20130.883, indicating moderate internal discrimination with substantial uncertainty.<\/p>\n<figure class=\"nds-roc-figure\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-testing-roc-2026.png\" alt=\"Testing ROC curve with area under the curve 0.756\"><figcaption>The orange point near 0.44 is selected from the same testing ROC curve and is descriptive only. The count table below uses the fixed article reference threshold 0.50.<\/figcaption><\/figure>\n<h3>Operating point at score 0.50<\/h3>\n<p>The fixed threshold produces 19 true positives, six false negatives, five false positives and nine true negatives.<\/p>\n<div class=\"nds-validation-grid\">\n<div class=\"nds-table-scroll\">\n<table class=\"nds-confusion\">\n<thead>\n<tr>\n<th>Actual \/ predicted<\/th>\n<th>Endpoint 1<\/th>\n<th>Endpoint 0<\/th>\n<th>Total<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>CIN II or worse<\/th>\n<td>19<\/td>\n<td>6<\/td>\n<td>25<\/td>\n<\/tr>\n<tr>\n<th>Negative or CIN I<\/th>\n<td>5<\/td>\n<td>9<\/td>\n<td>14<\/td>\n<\/tr>\n<tr>\n<th>Total<\/th>\n<td>24<\/td>\n<td>15<\/td>\n<td>39<\/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>Testing metric<\/th>\n<th>Value<\/th>\n<th>Count-based interpretation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Sensitivity<\/th>\n<td>76.0%<\/td>\n<td>19 of 25 endpoint-1 labels detected<\/td>\n<\/tr>\n<tr>\n<th>Specificity<\/th>\n<td>64.3%<\/td>\n<td>9 of 14 endpoint-0 labels rejected<\/td>\n<\/tr>\n<tr>\n<th>Precision \/ observed PPV<\/th>\n<td>79.2%<\/td>\n<td>19 of 24 positive calls match the source label<\/td>\n<\/tr>\n<tr>\n<th>Observed NPV<\/th>\n<td>60.0%<\/td>\n<td>9 of 15 negative calls match the source label<\/td>\n<\/tr>\n<tr>\n<th>Accuracy<\/th>\n<td>71.8%<\/td>\n<td>28 of 39 records classified correctly<\/td>\n<\/tr>\n<tr>\n<th>Balanced accuracy<\/th>\n<td>70.1%<\/td>\n<td>Mean of sensitivity and specificity<\/td>\n<\/tr>\n<tr>\n<th>F1 score<\/th>\n<td>0.776<\/td>\n<td>Summary of precision and sensitivity<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<div class=\"nds-figure-grid\"><\/div>\n<div class=\"nds-note\"><strong>Interpretation.<\/strong> These are internal, count-based results from only 39 records. Predictive values reflect this test subset&#8217;s 64.1% positive-label share and must not be transferred to a screening population. The test-derived ROC threshold cannot be called clinically optimal without external data and explicit consequences for false negatives and false positives.<\/div>\n<\/section>\n<section id=\"7-workflow\" class=\"nds-card\">\n<h2>7. Workflow and reproducibility<\/h2>\n<p>A responsible use of the artifact is a reproducibility workflow:<\/p>\n<div class=\"nds-flow\">\n<div>De-identified research record<\/div>\n<div>Schema and category checks<\/div>\n<div>Unknown-value audit<\/div>\n<div>Versioned model score<\/div>\n<div>Researcher comparison<\/div>\n<div>Independent clinical evidence<\/div>\n<\/div>\n<p>The calculator reproduces the final exported network for one complete source row. It deliberately returns a neutral score rather than a diagnosis or management recommendation.<\/p>\n<div id=\"nds-cervical-calculator\" class=\"nds-calculator\">\n<h3>Reproduce one exported model score<\/h3>\n<p>The default row is a complete source record used only to verify deployment. The calculation runs locally with the selected network&#8217;s exact preprocessing and coefficients.<\/p>\n<div class=\"nds-note\"><strong>Research demonstration.<\/strong> Values outside the validated domain are rejected. This model is not a diagnosis and must not be used to screen, determine follow-up or guide treatment.<\/div>\n<div class=\"nds-calculator-grid\"><label>Age (years)<input name=\"age\" type=\"number\" min=\"20\" max=\"68\" step=\"1\" value=\"46\" data-default=\"46\"><\/label><label>Cytology<select name=\"cytology\" data-default=\"lsil\"><option value=\"agc\">AGC<\/option><option value=\"asc_h\">ASC-H<\/option><option value=\"asc_us\">ASC-US<\/option><option value=\"hsil\">HSIL<\/option><option value=\"lsil\" selected>LSIL<\/option><option value=\"normal\">Normal<\/option><option value=\"unknown\">Unknown \/ not recorded<\/option><\/select><\/label><label>HPV risk group<select name=\"hpv_risk_group\" data-default=\"hpv_16_or_18\"><option value=\"hpv_16_or_18\" selected>HPV 16 or 18<\/option><option value=\"negative\">Negative<\/option><option value=\"other_high_risk\">Other high-risk HPV<\/option><option value=\"other_low_risk\">Other low-risk HPV<\/option><option value=\"unknown\">Unknown \/ not recorded<\/option><\/select><\/label><label>Biopsy result<select name=\"biopsy_result\" data-default=\"cin_2_3\"><option value=\"carcinoma\">Carcinoma<\/option><option value=\"cin_1\">CIN I<\/option><option value=\"cin_2\">CIN II<\/option><option value=\"cin_2_3\" selected>CIN II\u2013III<\/option><option value=\"cin_3\">CIN III<\/option><option value=\"negative_or_nondiagnostic\">Negative or nondiagnostic<\/option><option value=\"unknown\">Unknown \/ not recorded<\/option><\/select><\/label><label>p16\/Ki-67<select name=\"p16_ki67\" data-default=\"positive\"><option value=\"negative\">Negative<\/option><option value=\"positive\" selected>Positive<\/option><option value=\"unknown\">Unknown \/ not recorded<\/option><\/select><\/label><label>Smoking status<select name=\"smoking_status\" data-default=\"yes\"><option value=\"no\">No<\/option><option value=\"unknown\">Unknown \/ not recorded<\/option><option value=\"yes\" selected>Yes<\/option><\/select><\/label><\/div>\n<div class=\"nds-calculator-actions\"><button type=\"button\" data-action=\"calculate\">Calculate model score<\/button><button type=\"button\" class=\"nds-secondary-action\" data-action=\"reset\">Reset example<\/button><\/div>\n<div class=\"nds-calculator-error\" role=\"alert\" aria-live=\"assertive\"><\/div>\n<div class=\"nds-health-score\" aria-live=\"polite\"><\/div>\n<\/div>\n<p><script data-noptimize=\"1\">(()=>{const root=document.getElementById('nds-cervical-calculator');if(!root||root.dataset.ready==='1')return;root.dataset.ready='1';const c={\"categories\":{\"cytology\":[\"agc\",\"asc_h\",\"asc_us\",\"hsil\",\"lsil\",\"normal\",\"unknown\"],\"hpv_risk_group\":[\"hpv_16_or_18\",\"negative\",\"other_high_risk\",\"other_low_risk\",\"unknown\"],\"biopsy_result\":[\"carcinoma\",\"cin_1\",\"cin_2\",\"cin_2_3\",\"cin_3\",\"negative_or_nondiagnostic\",\"unknown\"],\"p16_ki67\":[\"negative\",\"positive\",\"unknown\"],\"smoking_status\":[\"no\",\"unknown\",\"yes\"]},\"w1\":[[-0.1477987617,-0.02237119153,0.09327578545,0.03902152181,0.1037407592,0.01211135462,-0.03181422502,-0.03450309113,0.4228804111,-0.149841547,-0.1396482289,0.0126877632,-0.009022255428,0.01291271206,-0.162186712,0.05537387729,0.4511473179,0.180283159,-0.5253175497,-0.03407342359,-0.1890487075,0.4272811413,-0.1552222222,-0.0680937767,-0.197261557,0.1950553209],[0.201605469,-0.08169293404,-0.1372248232,0.0389457196,-0.1639753729,0.1399687082,0.1048084348,-0.04466579482,-0.5052606463,0.2298371941,0.1586265415,0.04863918573,0.04182477668,0.01193062868,0.3622327149,-0.09122779965,-0.6913311481,-0.2661861777,0.67248559,0.01803127676,0.213017866,-0.5159108639,0.3489246368,0.0394430086,0.3182777166,-0.3677254617],[-0.1493650079,0.03812807798,0.0972956866,-0.1249115989,0.02985745668,-0.009405169636,-0.1503443718,0.01473321207,0.3339986801,-0.1184662208,-0.1158688292,-0.04197752848,0.009591665119,0.001826323103,-0.1477099806,0.1026507989,0.4225943685,0.1648899466,-0.4098890424,-0.01604230888,-0.1589151174,0.4155744314,-0.225081563,-0.07492762804,-0.2791436315,0.2577592731],[0.05482148379,-0.03279593214,-0.131563291,0.05850672349,-0.04132658243,0.09615985304,0.00210634619,-0.1006074697,-0.1263202876,0.05837222189,0.007842555642,-0.02541003376,0.004091480747,0.04665791243,0.08581557125,0.01463839412,-0.3376398683,-0.01698261127,0.3196594417,-0.09229738265,0.06582465023,-0.1947134733,0.1652893722,0.04293581843,0.2029862255,-0.1394062936],[0.2394592166,-0.009318254888,-0.2076590955,0.09913192689,-0.2684009373,0.08757392317,0.1401577741,-0.02446882799,-0.7149381638,0.3672426343,0.07587064803,-0.02098847181,0.009705599397,-0.06449465454,0.4042878151,-0.1853556484,-0.8930110931,-0.2984490991,0.7357635498,-0.08883829415,0.3680988848,-0.7493665218,0.3489682078,-0.0860644877,0.5100274086,-0.5221750736],[0.1863696575,0.03283989802,-0.07772260159,0.08927600086,-0.1569782794,-0.02743216604,0.03133622184,0.01580763981,-0.3086201549,0.1904358566,0.06064055115,-0.0321100466,-0.004049688112,-0.05143881589,0.2841241062,-0.05438724533,-0.5396775007,-0.1849818528,0.5078700185,-0.1065937728,0.2190520018,-0.4319725037,0.2959111333,0.02886730619,0.1992301643,-0.2522157431],[-0.2271161079,0.006855271757,0.2490539849,-0.1191394404,0.3006435633,-0.08664876223,-0.1943265051,0.07501414418,0.5016038418,-0.2563177347,-0.05746596679,0.03551650047,0.08099808544,0.03392632678,-0.2861194015,0.1360427737,0.8104064465,0.1790811718,-0.5784850121,0.1401295662,-0.1888280213,0.6240912676,-0.3199424148,0.02455235645,-0.3514756858,0.4035920501]],\"b1\":[-0.02196534723,0.02667379566,0.0100419363,0.01930290274,0.08899901807,0.03500841931,-0.08434604108],\"w2\":[-0.1550168693,-0.9021001458,0.4592923224,0.04639906809,-1.205117464,-0.04874119163,-0.6220618486],\"b2\":-0.483253181};const q=s=>root.querySelector(s);const error=q('.nds-calculator-error');const result=q('.nds-health-score');const render=score=>{const label=document.createElement('span');const value=document.createElement('strong');const detail=document.createElement('p');label.textContent='CIN II-or-worse source-label score';value.textContent=score.toFixed(6);detail.textContent='This uncalibrated research score reproduces the exported model. It is not an individual probability, diagnosis, screening result or management recommendation.';result.replaceChildren(label,value,detail);};const calculate=()=>{try{error.textContent='';const ageEl=q('[name=\"age\"]');const age=Number(ageEl.value);ageEl.classList.remove('is-invalid');if(!Number.isInteger(age)||age<20||age>68){ageEl.classList.add('is-invalid');throw new Error('Age must be an integer from 20 to 68 years.');}const values=[age];Object.entries(c.categories).forEach(([field,categories])=>{const selected=q('[name=\"'+field+'\"]').value;categories.forEach(category=>values.push(selected===category?1:0));});const scaled=[values[0]*.1030579284-4.09510994,...values.slice(1).map(value=>value*2-1)];const hidden=c.w1.map((weights,j)=>Math.tanh(c.b1[j]+weights.reduce((sum,weight,i)=>sum+weight*scaled[i],0)));const logit=c.b2+c.w2.reduce((sum,weight,i)=>sum+weight*hidden[i],0);render(1\/(1+Math.exp(-logit)));}catch(exception){error.textContent=exception.message;result.replaceChildren();}};q('[data-action=\"calculate\"]').addEventListener('click',calculate);q('[data-action=\"reset\"]').addEventListener('click',()=>{root.querySelectorAll('input,select').forEach(el=>{el.value=el.dataset.default;el.classList.remove('is-invalid');});calculate();});calculate();})();<\/script><\/p>\n<h3>Reproduce the calculation<\/h3>\n<p>The Python package contains the selected model, exact input order, categorical encoder and reference call. Neural Designer&#8217;s original generated code reused identifiers such as <code>unknown<\/code> and <code>negative<\/code> across fields; the downloadable reproducibility copy stores coefficients positionally to prevent category-name collisions while preserving the exact parameters.<\/p>\n<pre><code>from model import NeuralNetwork\n\nscore = NeuralNetwork().calculate_from_categories(\n    46, \"lsil\", \"hpv_16_or_18\", \"cin_2_3\", \"positive\", \"yes\"\n)\n# 0.7880622145<\/code><\/pre>\n<div class=\"nds-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-python-model-2026.zip\">Download Python model (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervical-cin2plus-neural-designer-project-2026.zip\">Download Neural Designer project (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/cervixcancer.csv\">Download cervixcancer.csv<\/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>No longitudinal endpoint.<\/strong> There is no documented index date, follow-up interval or later outcome, so this is not a prognosis or progression model.<\/li>\n<li><strong>Possible endpoint circularity.<\/strong> Biopsy is both an input and closely related to the derived lesion-grade target. Without timing and adjudication metadata, it may encode part of the reference standard rather than an upstream predictor.<\/li>\n<li><strong>Small, local sample.<\/strong> Only 197 records from one reported health area are available, with 39 testing rows and no external institution.<\/li>\n<li><strong>Missingness may encode care.<\/strong> p16\/Ki-67 and smoking are unknown in 39.6% and 52.3% of records. The unknown category can reflect which tests were ordered or recorded.<\/li>\n<li><strong>Duplicate leakage.<\/strong> Two testing input vectors occur in training; a grouped or temporal resplit is required.<\/li>\n<li><strong>No calibration or subgroup analysis.<\/strong> Scores are not calibrated, and performance by age, HPV group or other clinically relevant strata is not established.<\/li>\n<li><strong>No demonstrated added value.<\/strong> On this test subset, a simple biopsy rule slightly exceeds the neural network&#8217;s balanced accuracy.<\/li>\n<\/ul>\n<div class=\"nds-note nds-note--warning\"><strong>Decision boundary.<\/strong> Use this example for reproducible machine-learning education and retrospective method development only. Do not use it for screening, diagnosis, reassurance, treatment, referral or follow-up decisions. Any clinical investigation requires a prespecified intended use, temporally valid predictors, an independently adjudicated outcome, external validation, calibration, comparison with current care and clinician expert review.<\/div>\n<\/section>\n<section id=\"references\" class=\"nds-card\">\n<h2>References<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/cervical-cancer-prognosis\/\">Original Neural Designer example and stated Palencia data provenance<\/a>.<\/li>\n<li><a href=\"https:\/\/www.who.int\/publications\/i\/item\/9789240030824\">WHO guideline for screening and treatment of cervical pre-cancer lesions for cervical cancer prevention, second edition<\/a>.<\/li>\n<li><a href=\"https:\/\/www.who.int\/publications\/i\/item\/9789240121744\">WHO guideline on HPV DNA genotyping in cervical screening programmes<\/a>.<\/li>\n<li><a href=\"https:\/\/www.saludcastillayleon.es\/profesionales\/es\/programas-guias-clinicas\/programas-salud\/programa-prevencion-deteccion-precoz-cancer-cuello-utero-ca\">Castilla y Le\u00f3n cervical cancer prevention and early-detection programme<\/a>.<\/li>\n<\/ul>\n<\/section>\n<\/div>\n<\/div>\n","protected":false},"author":12,"featured_media":1322,"template":"","categories":[29],"tags":[38],"class_list":["post-3473","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\/ -->\n<title>Classify high-grade cervical lesions with machine learning<\/title>\n<meta name=\"description\" content=\"Build a prognosis model based on machine learning that focuses on the initial screening stages of cervical cancer.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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