International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Data Mining Approach to Identify Heart Disease Risk Factors in a Philippines Healthcare

Author(s) Ms. Kyla Trisha Dogmoc Abrea, Ms. Angie Imalay Cainoy, Ms. Marian Sandra Sumayang Doquila, Ms. Joyce Tacorda Gonzales
Country Philippines
Abstract The study wants to explore a data mining approach to identify heart disease risk factors in the Philippines using a relevant healthcare dataset. Using classification algorithms containing J48, Naïve Bayes, Random Forest, and PART, evaluated 13 patients' attributes to determine their predictive power. The data mining used Random Forest and found nine (9) primary factors that are notable predictors of heart disease in the datasets. These factors include (1) Thalassemia/Stress Test results (Thal), (2) Maximum Heart Rate (Thalach), (3) Chest Pain Type (CP), (4) Number of Major Vessels (CA), (5) ST Depression (Oldpeak), (6) Age, (7) Serum Cholesterol, (8) Resting Blood Pressure, and (9) Exercise-induced Angina. Among these factors, heart stress test results (Thal) and maximum heart rate (Thalach) were found to be the most critical clinical risk factors of heart disease. When it comes to model performance, Naive Bayes reached the highest accuracy of 83.11%, followed by Random Forest, which reached 78.75%. These results recommend that data mining be effective to prioritize the clinical factors. to allow healthcare providers to focus on the most critical diagnoses makers for early prevention in the Philippines for healthcare context.
Keywords Heart Disease Factors, Data Mining, Philippine Healthcare Datasets, WEKA (Waikato Environment for Knowledge Analysis), J48 Decision Tree, Naïve Bayes, Random Forest, PART (Partial Decision Tree)
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-13
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E-ISSN 2582-2160
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