APerformance Study of Selected Machine Learning Techniques for Predicting Heart Diseases
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Date
2025-04
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Springer
Abstract
Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR
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Olorunfemi, B. O., Ogunde, A. O., Adeniyi, A. E., Awotunde, J. B., Imoize, A. L., & Li, C.-T. (2025). A performance study of selected machine learning techniques for predicting heart diseases. In G. A. Tsihrintzis et al. (Eds.), Security and information technologies with AI, Internet computing and big-data applications (Smart Innovation, Systems and Technologies, Vol. 410). Springer. https://doi.org/10.1007/978-981-97-7786-0_27