{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/heart-disease-prediction-system-using","title":"Heart Disease Prediction System using Associative Classification and Genetic Algorithm","arxiv_id":"1303.5919","date":"2013-03-24","proceeding":null,"authors":["M. Akhil Jabbar","B L Deekshatulu","Priti Chandra"],"abstract":"Associative classification is a recent and rewarding technique which\nintegrates association rule mining and classification to a model for prediction\nand achieves maximum accuracy. Associative classifiers are especially fit to\napplications where maximum accuracy is desired to a model for prediction. There\nare many domains such as medical where the maximum accuracy of the model is\ndesired. Heart disease is a single largest cause of death in developed\ncountries and one of the main contributors to disease burden in developing\ncountries. Mortality data from the registrar general of India shows that heart\ndisease are a major cause of death in India, and in Andhra Pradesh coronary\nheart disease cause about 30%of deaths in rural areas. Hence there is a need to\ndevelop a decision support system for predicting heart disease of a patient. In\nthis paper we propose efficient associative classification algorithm using\ngenetic approach for heart disease prediction. The main motivation for using\ngenetic algorithm in the discovery of high level prediction rules is that the\ndiscovered rules are highly comprehensible, having high predictive accuracy and\nof high interestingness values. Experimental Results show that most of the\nclassifier rules help in the best prediction of heart disease which even helps\ndoctors in their diagnosis decisions.","url_abs":"http://arxiv.org/abs/1303.5919v1","url_pdf":"http://arxiv.org/pdf/1303.5919v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"heart-disease-prediction-system-using","repo_url":"https://github.com/Divyansh898/Compiler-Lab-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"disease-prediction","task_name":"Disease Prediction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}