Abstract
Cardiovascular illness is one of the prime sources for the loss of many human lives every year. The prime cause for mortality is the absence of past diagnostic information. Hence, for better prediction of the disease, many researchers have proposed this reason allows the researchers to build new and optimized machine learning models for betterment in prediction accuracy of such diseases with low false-negative rate. Here, a voting method is applied which uses Logistic Regression, Random Forest, XGBoost, and Naive Bayes to predict the nature of heart conditions. The system is trained, tested, and evaluated based on accuracy, precision, F1 score, Recall, and AUC. Testing witnessed an accuracy of 78%. The results show that ensemble modeling is more accurate when compared to individual models.
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Swain, D., Kava, M., Jadhav, R., Satapathy, S. (2023). A Novel Approach to Predicting the Cardiovascular Sickness. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_4
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DOI: https://doi.org/10.1007/978-981-99-3656-4_4
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