{"doi":"10.1089/met.2019.0104","title":"Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population","abstract":"<jats:sec>\n                    <jats:title>Aim:</jats:title>\n                    <jats:p>The primary objective of our research was to compare the performance of data analysis to predict vitamin D deficiency using three different regression approaches and to evaluate the usefulness of incorporating machine learning algorithms into the data analysis in a clinical setting.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:</jats:title>\n                    <jats:p>We included 221 patients from our hypertension unit, whose data were collected from electronic records dated between 2006 and 2017. We used classical stepwise logistic regression, and two machine learning methods [least absolute shrinkage and selection operator (LASSO) and elastic net]. We assessed the performance of these three algorithms in terms of sensitivity, specificity, misclassification error, and area under the curve (AUC).</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:</jats:title>\n                    <jats:p>LASSO and elastic net regression performed better than logistic regression in terms of AUC, which was significantly better in both penalized methods, with AUC = 0.76 and AUC = 0.74 for elastic net and LASSO, respectively, than in logistic regression, with AUC = 0.64. In terms of misclassification rate, elastic net (18%) outperformed LASSO (22%) and logistic regression (25%).</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:</jats:title>\n                    <jats:p>Compared with a classical logistic regression approach, penalized methods were found to have better performance in predicting vitamin D deficiency. The use of machine learning algorithms such as LASSO and elastic net may significantly improve the prediction of vitamin D deficiency in a hypertensive obese population.</jats:p>\n                  </jats:sec>","journal":"Metabolic Syndrome and Related Disorders","year":2020,"id":46546,"datarank":1.1628629700619038,"base_score":3.258096538021482,"endowment":3.258096538021482,"self_citation_contribution":0.4887144807032224,"citation_network_contribution":0.6741484893586813,"self_endowment_contribution":0.4887144807032224,"citer_contribution":0.6741484893586813,"corpus_percentile":null,"corpus_rank":null,"citation_count":25,"citer_count":21,"citers_with_citation_signal":20,"citers_with_endowment":20,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":215702,"name":"Luis Vigil-Medina","orcid":null,"position":1,"is_corresponding":false},{"id":215703,"name":"Oscar Barquero-Perez","orcid":null,"position":2,"is_corresponding":false},{"id":215704,"name":"Inmaculada Mora-Jimenez","orcid":null,"position":3,"is_corresponding":false},{"id":215705,"name":"Cristina Soguero-Ruiz","orcid":null,"position":4,"is_corresponding":false},{"id":215706,"name":"Rebeca Goya-Esteban","orcid":null,"position":5,"is_corresponding":false},{"id":215707,"name":"Javier Ramos-Lopez","orcid":null,"position":6,"is_corresponding":false},{"id":215701,"name":"Rafael Garcia-Carretero","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population","abstract":"<jats:sec>\n                    <jats:title>Aim:</jats:title>\n                    <jats:p>The primary objective of our research was to compare the performance of data analysis to predict vitamin D deficiency using three different regression approaches and to evaluate the usefulness of incorporating machine learning algorithms into the data analysis in a clinical setting.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:</jats:title>\n                    <jats:p>We included 221 patients from our hypertension unit, whose data were collected from electronic records dated between 2006 and 2017. We used classical stepwise logistic regression, and two machine learning methods [least absolute shrinkage and selection operator (LASSO) and elastic net]. We assessed the performance of these three algorithms in terms of sensitivity, specificity, misclassification error, and area under the curve (AUC).</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:</jats:title>\n                    <jats:p>LASSO and elastic net regression performed better than logistic regression in terms of AUC, which was significantly better in both penalized methods, with AUC = 0.76 and AUC = 0.74 for elastic net and LASSO, respectively, than in logistic regression, with AUC = 0.64. In terms of misclassification rate, elastic net (18%) outperformed LASSO (22%) and logistic regression (25%).</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:</jats:title>\n                    <jats:p>Compared with a classical logistic regression approach, penalized methods were found to have better performance in predicting vitamin D deficiency. The use of machine learning algorithms such as LASSO and elastic net may significantly improve the prediction of vitamin D deficiency in a hypertensive obese population.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":3.258096538021482,"endowment":3.258096538021482,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31928513","pmcid":null,"openalex_id":"https://openalex.org/W2999956516","authors":[],"funders":[],"total_grants":0,"fwci":1.7145,"citation_percentile":0.83892385,"influential_citations":0,"citation_trend":[{"year":2020,"count":1},{"year":2021,"count":4},{"year":2022,"count":3},{"year":2023,"count":4},{"year":2024,"count":6},{"year":2025,"count":6},{"year":2026,"count":1}],"oa_status":"closed","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://journals.sagepub.com/doi/full-xml/10.1089/met.2019.0104","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/pdf/10.1089/met.2019.0104","host_type":"publisher"},{"url":"https://doi.org/10.1089/met.2019.0104","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31928513","host_type":"repository"}],"fields_of_study":["Vitamin D Research Studies","Nutritional Studies and Diet","Medicine","Biomarkers","Cross-Sectional Studies","Data Mining","Electronic Health Records","Female","Humans","Hypertension","Logistic Models","Machine Learning","Male","Middle Aged","Obesity","Prevalence","Retrospective Studies","Risk Assessment","Risk Factors","Spain","Vitamin D Deficiency"],"mesh_terms":["Machine Learning","Cross-Sectional Studies","Female","Humans","Hypertension","Male","Middle Aged","Obesity","Retrospective Studies","Risk Factors","Spain","Vitamin D Deficiency","Biomarkers","Prevalence","Logistic Models","Risk Assessment","Data Mining","Electronic Health Records"],"keywords":["Elastic net regularization","Lasso (programming language)","Logistic regression","Medicine","Machine learning","Population","Stepwise regression","Regression","Artificial intelligence","Regression analysis","Statistics","Algorithm","Internal medicine","Mathematics","Computer science","Vitamin D","Obesity","metabolic syndrome","Penalized Regression"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Zero hunger"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-10T21:39:30.567677Z","pmid":null,"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":[]}