Heart Disease Prediction Using GridSearchCV and Random Forest
DOI:
https://doi.org/10.4108/eetpht.10.5523Keywords:
AdaBoost Classifier, AB, Cross-Validation Methods, Data Preprocessing Techniques, Early Diagnosis Models, Healthcare Analytics, Logistic Regression, LR, Naive Bayes Classifier, NB, Random Forest Algorithm, RF, Support Vector Machines, SVMAbstract
INTRODUCTION: This study explores machine learning algorithms (SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest) for heart disease prediction, utilizing comprehensive cardiovascular and clinical data. Our research enables early detection, aiding timely interventions and preventive measures. Hyperparameter tuning via GridSearchCV enhances model accuracy, reducing heart disease's burdens. Methodology includes preprocessing, feature engineering, model training, and cross-validation. Results favor Random Forest for heart disease prediction, promising clinical applications. This work advances predictive healthcare analytics, highlighting machine learning's pivotal role. Our findings have implications for healthcare and policy, advocating efficient predictive models for early heart disease management. Advanced analytics can save lives, cut costs, and elevate care quality.
OBJECTIVES: Evaluate the models to enable early detection, timely interventions, and preventive measures.
METHODS: Utilize GridSearchCV for hyperparameter tuning to enhance model accuracy. Employ preprocessing, feature engineering, model training, and cross-validation methodologies. Evaluate the performance of SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest algorithms.
RESULTS: The study reveals Random Forest as the favored algorithm for heart disease prediction, showing promise for clinical applications. Advanced analytics and hyperparameter tuning contribute to improved model accuracy, reducing the burden of heart disease.
CONCLUSION: The research underscores machine learning's pivotal role in predictive healthcare analytics, advocating efficient models for early heart disease management.
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Chakraborty, C. and Kishor, A., 2022. Real-time cloud-based patient-centric monitoring using computational health systems. IEEE transactions on computational social systems, 9(6), pp.1613-1623. DOI: https://doi.org/10.1109/TCSS.2022.3170375
Shao, S., Wang, T., Mumtaz, A., Song, C. and Yao, C., 2022. Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network. IEEE Journal of Biomedical and Health Informatics, 27(4), pp.1670-1680. DOI: https://doi.org/10.1109/JBHI.2022.3162894
Tang, Y., Brown, S.M., Sorensen, J. and Harley, J.B., 2020. Physiology-informed real-time mean arterial blood pressure learning and prediction for septic patients receiving norepinephrine. IEEE Transactions on Biomedical Engineering, 68(1), pp.181-191. DOI: https://doi.org/10.1109/TBME.2020.2997929
Abubaker, M.B. and Babayiğit, B., 2022. Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods. IEEE Transactions on Artificial Intelligence, 4(2), pp.373-382. DOI: https://doi.org/10.1109/TAI.2022.3159505
Petrou, A., Kanakis, M., Magkoutas, K., De Vries, B., Meboldt, M. and Daners, M.S., 2020. Cardiac output estimation: Online implementation for left ventricular assist device support. IEEE Transactions on Biomedical Engineering, 68(6), pp.1990-1998. DOI: https://doi.org/10.1109/TBME.2020.3045879
Deb, A., Koli, M.S.A., Akter, S.B. and Chowdhury, A.A., 2022, June. An Outcome Based Analysis on Heart Disease Prediction using Machine Learning Algorithms and Data Mining Approaches. In 2022 IEEE World AI IoT Congress (AIIoT) (pp. 01-07). IEEE. DOI: https://doi.org/10.1109/AIIoT54504.2022.9817194
Ahmad, G.N., Fatima, H., Ullah, S. and Saidi, A.S., 2022. Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV. IEEE Access, 10, pp.80151-80173. DOI: https://doi.org/10.1109/ACCESS.2022.3165792
Hasan, R., 2021. Comparative analysis of machine learning algorithms for heart disease prediction. In ITM Web of Conferences (Vol. 40, p. 03007). EDP Sciences. DOI: https://doi.org/10.1051/itmconf/20214003007
Rasheed, M., Khan, M.A., Elmitwally, N.S., Issa, G.F., Ghazal, T.M., Alrababah, H. and Mago, B., 2022, October. Heart disease prediction using machine learning method. In 2022 International Conference on Cyber Resilience (ICCR) (pp. 1-6). IEEE.14 DOI: https://doi.org/10.1109/ICCR56254.2022.9995736
Chandrasekhar, N. and Peddakrishna, S., 2023. Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes, 11(4), p.1210. DOI: https://doi.org/10.3390/pr11041210
Khan, A., Qureshi, M., Daniyal, M. and Tawiah, K., 2023. A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction. Health & Social Care in the Community, 2023. DOI: https://doi.org/10.1155/2023/1406060
Jubier Ali, M., Chandra Das, B., Saha, S., Biswas, A.A. and Chakraborty, P., 2022. A comparative study of machine learning algorithms to detect cardiovascular disease with feature selection method. In Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2021 (pp. 573-586). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-19-2347-0_45
Al Ahdal, A., Rakhra, M., Badotra, S. and Fadhaeel, T., 2022, March. An integrated machine learning techniques for accurate heart disease prediction. In 2022 International Mobile and Embedded Technology Conference (MECON) (pp. 594- 598). IEEE. DOI: https://doi.org/10.1109/MECON53876.2022.9752342
Mamun, M., Uddin, M.M., Tiwari, V.K., Islam, A.M. and Ferdous, A.U., 2022, October. MLHeartDis: Can Machine Learning Techniques Enable to Predict Heart Diseases?. In 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0561-0565). IEEE. DOI: https://doi.org/10.1109/UEMCON54665.2022.9965714
Muhammed, S.M., Abdul-Majeed, G. and Mahmoud, M.S., 2023. Prediction of Heart Diseases by Using Supervised Machine Learning Algorithms. Wasit Journal of Pure sciences, 2(1), pp.231-243. DOI: https://doi.org/10.31185/wjps.125
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Copyright (c) 2024 Shagufta Rasheed, G Kiran Kumar, D Malathi Rani, M V V Prasad Kantipudi, Anila M
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