{"doi":"10.1371/journal.pone.0265209","title":"Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records","abstract":"BACKGROUND AND AIMS: The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors. METHODS: We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression). RESULTS: The study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48-1.00) vs. reference: 0.43 (0.18-0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59-0.69) vs. reference: 0.55 (0.50-0.59); P<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles. DISCUSSION: Machine learning can predict CRC risk in adults aged 35-50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.","journal":"PLoS ONE","year":2022,"id":257356,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9371,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":907318,"name":"Jing Zhao","orcid":"0000-0003-1016-6373","position":1,"is_corresponding":false},{"id":748291,"name":"Abraham K. Badu‐Tawiah","orcid":"0000-0001-8642-3431","position":2,"is_corresponding":false},{"id":907319,"name":"Peter P. Stanich","orcid":"0000-0002-6844-640X","position":3,"is_corresponding":false},{"id":230958,"name":"Fred K. Tabung","orcid":"0000-0001-8193-7150","position":4,"is_corresponding":false},{"id":383818,"name":"Darrell M. Gray","orcid":"0000-0003-2506-3465","position":5,"is_corresponding":false},{"id":235827,"name":"Qin Ma","orcid":"0000-0002-3264-8392","position":6,"is_corresponding":false},{"id":598459,"name":"Matthew F. Kalady","orcid":"0000-0002-2114-114X","position":7,"is_corresponding":false},{"id":366108,"name":"Steven K. Clinton","orcid":"0000-0002-2840-5865","position":8,"is_corresponding":false},{"id":692020,"name":"Hisham Hussan","orcid":"0000-0002-8646-8370","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T00:25:30.183987Z","pmid":"35271664","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}