{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:04:23Z","timestamp":1777705463736,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>This paper addressed the prediction of heart sicknesses from hazard elements through a decision-making tree. We introduced the facts mining technique in public fitness to extract high-degree knowledge from raw data, which facilitates predicting heart diseases from risk factors and their prevention. The existing work intends to introduce a new risk element in heart diseases using novel data mining strategies. Latest actual international affected person\u2019s information (e.g., smoking, area of residence, age, weight, blood stress, chest pain, low-density lipoproteins (LDL), high-density lipoproteins (HDL), block arteries became accrued by way of the use of questionnaire through direct interview technique from patients. Novel two-variable decision trees are constructed for coronary heart illness records primarily based on chance factors and ranking of risk elements. The results show a correct prediction of cardiovascular disease (CVD) from the risk factor if records on chance factors are available as direct results of this study, tobacco, loss of physical exercise, and weight-reduction plan play a vital role in predicting heart diseases, which is the most important reason for mortality in developing countries, especially in my country.<\/jats:p>","DOI":"10.3233\/jifs-202226","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T14:14:22Z","timestamp":1630419262000},"page":"5985-6002","source":"Crossref","is-referenced-by-count":1,"title":["A Machine learning-based prediction model for the heart diseases from chance factors through two-variable decision tree classifier"],"prefix":"10.1177","volume":"41","author":[{"given":"Y.","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Sciences and Engineering, Shandong Agricultural University China, P.R. 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