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Feature Selection and Hyper-Parameter Tuning for Optimizing Decision Tree Algorithm on Heart Disease Classification


Tsehay Admassu Assegie, Sushma S.J, Bhavya B.G, and Padmashree S


Vol. 24  No. 2  pp. 150-154


In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.


Heart disease classification, Feature selection, Chi-squared test, Parameter tuning, optimizing decision tree.