{"doi":"10.18137/cardiometry.2022.25.788793","title":"Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm","abstract":"<jats:p>Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Forest is better than Naive Bayes algorithm. Conclusion: From the result, it can be concluded that Novel Random Forest helps in predicting heart disease with more accuracy compared to Naive Bayes algorithm.</jats:p>","journal":"CARDIOMETRY","year":2023,"id":46732,"datarank":0.12238379906761536,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.018411721983623546,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.018411721983623546,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"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":216270,"name":"S. Sivaprasad","orcid":null,"position":1,"is_corresponding":false},{"id":216269,"name":"G. Pavithraa","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm","abstract":"<jats:p>Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Forest is better than Naive Bayes algorithm. Conclusion: From the result, it can be concluded that Novel Random Forest helps in predicting heart disease with more accuracy compared to Naive Bayes algorithm.</jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"18998783","pmcid":null,"openalex_id":"https://openalex.org/W4367335949","authors":[],"funders":[],"total_grants":0,"fwci":0.3299,"citation_percentile":0.66440319,"influential_citations":0,"citation_trend":[{"year":2025,"count":1}],"oa_status":"gold","license":null,"oa_locations":[{"url":"https://s3.timeweb.com/cm94660-20b5ac41-549d-43b0-a32e-a074405a8023/eng/issues/no25-december-2022/naive-bayes-algorithm.pdf","host_type":"journal"},{"url":"https://s3.timeweb.com/cm94660-20b5ac41-549d-43b0-a32e-a074405a8023/eng/issues/no25-december-2022/naive-bayes-algorithm.pdf","host_type":"publisher"},{"url":"https://doi.org/10.18137/cardiometry.2022.25.788793","host_type":"journal"}],"fields_of_study":["Artificial Intelligence in Healthcare","Imbalanced Data Classification Techniques","Digital Imaging for Blood Diseases"],"mesh_terms":[],"keywords":["Naive Bayes classifier","Random forest","Bayes' theorem","Bayes error rate","Computer science","Algorithm","Artificial intelligence","Statistics","Machine learning","Bayes classifier","Mathematics","Bayesian probability","Support vector machine"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life in Land"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-13T08:39:44.470490Z","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":[]}