{"doi":"10.35940/ijrte.e1013.0285s20","title":"Heart Disease Prediction using Machine Learning Models","abstract":"<jats:p>Healthcare has become one of the most important concerns in the world. The cases of heart disease are increasing on a rapid scale among the people especially among the young generation. We can save the lives of the people if we could detect the heart disease on/before time, by getting them treated. In this matter artificial intelligence can be of a great help. Here we have collected a data set and then we have built a prediction model to detect heart disease based on the various algorithms that are available for machine learning.we have used Logistic regression, K-NN, SVM, Decision Tree, Random Forest with the accuracy values of K-Neighbors Classifier (0.956194%), Support Vector Machine (0.9561945%), Decision Tree (0.91050%), Random Forest Classifier (0.95404%) and Logistic Regression (0.95592%). The best value given by the Machine Learning model is by Logistic regression followed by K-NN.</jats:p>","journal":"International Journal of Recent Technology and Engineering","year":2020,"id":669377,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":[],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Heart Disease Prediction using Machine Learning Models","abstract":"<jats:p>Healthcare has become one of the most important concerns in the world. The cases of heart disease are increasing on a rapid scale among the people especially among the young generation. We can save the lives of the people if we could detect the heart disease on/before time, by getting them treated. In this matter artificial intelligence can be of a great help. Here we have collected a data set and then we have built a prediction model to detect heart disease based on the various algorithms that are available for machine learning.we have used Logistic regression, K-NN, SVM, Decision Tree, Random Forest with the accuracy values of K-Neighbors Classifier (0.956194%), Support Vector Machine (0.9561945%), Decision Tree (0.91050%), Random Forest Classifier (0.95404%) and Logistic Regression (0.95592%). The best value given by the Machine Learning model is by Logistic regression followed by K-NN.</jats:p>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W4239706975","authors":[],"funders":[],"total_grants":0,"fwci":0.9235,"citation_percentile":0.85714286,"influential_citations":0,"citation_trend":[{"year":2021,"count":2},{"year":2022,"count":1},{"year":2024,"count":1}],"oa_status":"gold","license":null,"oa_locations":[{"url":"https://doi.org/10.35940/ijrte.e1013.0285s20","host_type":"journal"},{"url":"https://doi.org/10.35940/ijrte.e1013.0285s20","host_type":"publisher"},{"url":"https://www.ijrte.org/wp-content/uploads/papers/v8i5s/E10130285S20.pdf","host_type":"publisher"}],"fields_of_study":["Artificial Intelligence in Healthcare"],"mesh_terms":[],"keywords":["Logistic regression","Random forest","Decision tree","Machine learning","Artificial intelligence","Support vector machine","Classifier (UML)","Logistic model tree","Computer science","Heart disease","Decision tree learning","Medicine","Internal medicine"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-14T17:54:04.984924Z","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":[]}