Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction
Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.
Li, J. P., Haq, A. U., Din, S. U., Khan, J., Khan, A., & Saboor, A. (2020). Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access,Vol. 8, 107562-107582.
Ali, L., Niamat, A., Khan, J. A., Golilarz, N. A., Xingzhong, X., Noor, A., & Bukhari, S. A. C. (2019). An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access, Vol.7, 54007-54014.
Beunza, J. J., Puertas, E., García-Ovejero, E., Villalba, G., Condes, E., Koleva, G., & Landecho, M. F. (2019). Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). Journal of biomedical informatics, Vol.97, 103257.
Motarwar, P., Duraphe, A., Suganya, G., & Premalatha, M. (2020, February). Cognitive Approach for Heart Disease Prediction using Machine Learning. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)IEEE, 1-5
Mantovani, R. G., Horváth, T., Cerri, R., Vanschoren, J., & de Carvalho, A. C. (2016, October). Hyper-parameter tuning of a decision tree induction algorithm. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS) IEEE, 37-42
Ali, A. A. (2019). Stroke Prediction using Distributed Machine Learning Based on Apache Spark. Stroke, Vol.28, No.15, 89-97.
Chandrasegar, T., & Choudhary, A. (2019, March). Heart Disease Diagnosis using a Machine Learning Algorithm. In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, Vol. 1, 1-4).
Latha, C. B. C., & Jeeva, S. C. (2019). Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, Vol.16, 100203.
Singh, D., & Samagh, J. S. (2020). A COMPREHENSIVE REVIEW OF HEART DISEASE PREDICTION USING MACHINE LEARNING. Journal of Critical Reviews, Vol.7, No.12, 2020.
Ayon, S. I., Islam, M. M., & Hossain, M. R. (2020). Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE Journal of Research, 1-20.
Ahmed, H., Younis, E. M., Hendawi, A., & Ali, A. A. (2020). Heart disease identification from patients' social posts, machine learning solution on Spark. Future Generation Computer Systems, Vol.111, 714-722.
Alizadehsani, R., Abdar, M., Roshanzamir, M., Khosravi, A., Kebria, P. M., Khozeimeh, F., & Acharya, U. R. (2019). Machine learning-based coronary artery disease diagnosis: A comprehensive review. Computers in Biology and Medicine, Vol.111, 103346.
Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, 82-93.
Bashir, S., Khan, Z. S., Khan, F. H., Anjum, A., & Bashir, K. (2019, January). Improving heart disease prediction using feature selection approaches. In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), IEEE, 619-623
Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554.
Wu, C. S. M., Badshah, M., & Bhagwat, V. (2019, July). Heart Disease Prediction Using Data Mining Techniques. In Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, 7-11.
Reddy, N. S. C., Nee, S. S., Min, L. Z., & Ying, C. X. (2019). Classification and feature selection approaches by machine learning techniques: Heart disease prediction. International Journal of Innovative Computing, Vol.9, No.1.
Ziasabounchi, N., & Askerzade, I. (2014). ANFIS based classification model for heart disease prediction. International Journal of Electrical & Computer Sciences IJECS-IJENS, Vol.14, No.02, 7-12.
Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018). A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems, 2018.
Fitriyani, N. L., Syafrudin, M., Alfian, G., & Rhee, J. (2020). HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System. IEEE Access, 8, 133034-133050.
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