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A Predictive Model for Heart Disease Detection Using Data Mining Techniques

Jakkrit Premsmith and Hathairat Ketmaneechairat
College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Thailand

Abstract—In this paper, the model is proposed to predict the heart disease detection by using data mining techniques. The data mining algorithm uses the Logistic Regression model and Neural Network model. The dataset of this paper uses the heart disease data at the University of California Irvine (UCI). There are a total of 303 Instances and 75 Attributes in the United States. The evaluation criteria using the confusion matrix table such as accuracy, precision, recall and F-Measure. The results show that the Logistic Regression model is better performance than Neural Network model. The Logistic Regression model has 95.45% precision and 91.65% accuracy. The web application can be support for the user, who wants to diagnose heart disease detection.
Index Terms—heart disease, predictive model, detection, data mining, logistic regression, neural network

Cite: Jakkrit Premsmith and Hathairat Ketmaneechairat, "A Predictive Model for Heart Disease Detection Using Data Mining Techniques," Journal of Advances in Information Technology, Vol. 12, No. 1, pp. 14-20, February 2021. doi: 10.12720/jait.12.1.14-20

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.