Development of Risk Predictive Model for Heart Attack in Elderly Using Data Mining Technique
Keywords:
Heart Attack, Support Vector Machine, Naive Bayes, Decision TreeAbstract
The objectives of this research are 1) to compare risk predictive model performance for heart attack in the elderly using data mining techniques, and 2) to develop a prototype system for predicting the risk of heart attack in the elderly, using the heart attack dataset in the Heart Attack Analysis & Prediction Dataset. This research used the Scikit-Learn Library in Python to create the models. Three classifiers in machine learning, namely Support Vector Machine, Naive Bayes, and Decision Tree were used for comparison. The performance of the models was evaluated using accuracy, precision, recall, and F-measure. The research found that Naive Bayes had the highest performance with an accuracy of 86.05%, precision of 87.44%, recall of 85.82%, and F-measure of 85.86%. Naive Bayes was then used to develop a prototype system for predicting heart attacks in the elderly through web application, to Predict whether the chance of having a heart attack is more or less.
References
Kaewna, P. (2023). Factors associated with Streptokinase failure in the treatment of STEMI patients in District hospital: a retrospective study. Academic Journal of Mahasarakham Provincial Public Health Office, 7(14), 113-122.
Mannaisatjatham, L. (2022). Simulation of Oncology Drugs Inventory Controls by Drug Inventory Department Using Vendor Managed Inventory through Decision Trees Classification in National Cancer Institute of Thailand. Journal of The Department of Medical Services, 46 (4), 106-14.
Matachalearmpat, R. (2022). Application of Machine Learning with industrial work. National Electronics and Computer Technology Center.
Njoku O.C. (2019). Decision Trees and Their Application for Classification and Regression Problems. (Graduate Theses). United State: Missouri State University.
Panyatip, T. et al. (2022). Optimization Classification of Covid-19 effect on Liver Cancer. Journal of Technology Management Rajabhat Maha Sarakham University, 9(2), 66–78.
Petwised, W. (2023). Development of a Models to Predict Colorectal Cancer Warning Sign. Journal of Science and Technology, Rajabhat Maha Sarakham University 6 (1). Maha Sarakham, (75-87).
Rungrattanaubol, J. (2023). Data Mining Techniques. Naresuan University Publishing House, Phitsanulok.
Suesat, T. et al. (2020). Factors Associated with Treatment Result of Acute Coronary Syndrome Patients. KKU Journal for Public Health Research, 13(2), 102–112.
Sinsomboonthong, S. (2017). Data mining 1: discovering knowledge in data. Bangkok: Chamchuree Products Co.,Ltd.
Tosasukul, J. (2021). A Prediction Modelling for Dengue Fever Outbreaks Using Data Mining Techniques. PKRU SciTech Journal 5 (2), 51-60.
Widyawati, D. et al. (2023). Comparison Analysis of Classification Model Performance in Lung Cancer Prediction Using Decision Tree, Naive Bayes, and Support Vector Machine. Indonesian Journal of Data and Science, 4(2), 78-86.
