Predictive Models for Heart Attack Disease Risk

  • Iyabo Olukemi Awoyelu Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • Oluwadamilola Egbekunle Obafemi Awolowo University, Ile-ife
  • Oluwadare Ogunlade Department of Physiological Sciences, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
Keywords: Decision-Tree, Bagging, Naïve-Bayes, Feature-Selection, Information-Gain-Ratio, Wrapper, Heart-attack


Data mining techniques have been widely applied to the diagnosis of heart diseases. This paper develops models that predict the risk of an individual developing a heart attack. This is with a view to formulating a predictive model to determine the likelihood of having heart attack in a patient, simulating the model formulated, and evaluating the performance of the model. The predictive models were formulated and simulated using Rapid Miner Studio - a collection of knowledge discovery and machine learning tools. The models were developed using Decision-tree, Naïve-Bayes, and Bagging classifiers. The inputs for these prediction models were risk factors of heart attack. A hybrid feature selection technique was carried out and the features selected were used as input variables to the classifiers. 10-fold cross validation was used to assess the performance of the algorithms in predicting a class. The models’ performance was evaluated using accuracy and sensitivity as metrics. The Naïve Bayes model exhibits a better performance with an accuracy of 87.86%. The model is expected to enhance the decision-making process of cardiologist.


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How to Cite
Awoyelu, I. O., Egbekunle , O., & Ogunlade, O. (2020). Predictive Models for Heart Attack Disease Risk. Ife Journal of Technology, 27(1), 60-70. Retrieved from