Heart Disease Prediction Using Ensemble Model and Hyperparameter Optimization
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Abstract
Heart disease is a major global health concern that responsible for significant mortality rates, killing 17.9 million people each year on average. To overcome this problem, machine learning can assist in forecasting the occurrence of heart disease, aiding in its prevention and treatment. This paper explores several classification models to forecast heart disease. This paper also utilizes the hyperparameter tuning method via grid search cv to enhance the accuracy of the models. Finally, the experiment concludes with an ensemble vote on all hyperparameter-tuned classification models. The x-gradient boost and random forest classifier deliver the best outcomes, with an accuracy of 88.04% and 89.13% before hyperparameter optimization, and 92.39% after hyperparameter optimization. These results show that machine learning models are capable of forecasting the risk of heart disease. These models may assist healthcare professionals in identifying individuals at risk of heart disease, enabling preventative measures to be taken. It is essential to note that this study focuses solely on classification models and may not represent the entire population. Further research is required to determine the predictability of heart disease in diverse populations.
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References
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