{"id":25231,"date":"2026-04-01T17:33:39","date_gmt":"2026-04-01T17:33:39","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=25231"},"modified":"2026-04-01T17:33:39","modified_gmt":"2026-04-01T17:33:39","slug":"enhancing-clinical-trust-in-heart-disease-diagnosis-from-data-to-decisions-with-ml-models-and-lime-explanations","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/enhancing-clinical-trust-in-heart-disease-diagnosis-from-data-to-decisions-with-ml-models-and-lime-explanations\/","title":{"rendered":"Enhancing clinical trust in heart disease diagnosis: From data to decisions with ML models and LIME explanations"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">Heart disease represents a significant health challenge impacting both women and men worldwide, requiring early and accurate prediction to enhance patient outcomes. Medical conditions such as high blood pressure, diabetes, and kidney disease can impair the prognosis of heart failure. People with a family history of heart disease are at a drastically increased risk even with a healthy lifestyle. The analysis of heart disease data poses difficulties owing to the interrelations among diverse metrics and the elevated complexity of the data. Current diagnostic methods, including electrocardiogram ECG and blood tests, are widely used but might not predict future risk very well, showcasing the need for Artificial Intelligence (AI) based approaches. AI-based techniques have demonstrated usefulness in recent years in enhancing comprehension of the significance of these characteristics. Machine learning (ML) has become very significant in the current era of disease diagnosis. ML speeds up diagnosis of diseases and increases accuracy. By analyzing vast datasets, ML models help identify hidden patterns in patient history. Through analysis of individual patient-specific data, ML can help physicians design personalized treatment plans according to each individual risk factors. Different ensemble ML techniques are employed in this work for classification. It uses local interpretable model-agnostic explanations (LIME) method for feature selection. In this work we are using heart dataset from Kaggle. Among the different ensemble classifiers, Random Forest (RF) obtained an accuracy value of 93.17% which is the highest. Experimental results confirm the efficacy of the suggested approach in classifying heart disease.<\/div>\n","protected":false},"featured_media":25196,"template":"","meta":{"_acf_changed":false},"journal-name":[218],"paper-tag":[233,269],"class_list":["post-25231","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-cjmss","paper-tag-issue-1","paper-tag-volume-5"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/25231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media\/25196"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=25231"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=25231"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=25231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}