Abstract
One of the most serious health issues affecting people today is heart disease. Identifying cardiac disease can be challenging as several common risk elements including high cholesterol, diabetes, irregular heart rate, high blood pressure, and various medical conditions can make diagnosis difficult. Due to these limitations, researchers are increasingly adopting cutting-edge techniques like machine learning and data mining to forecast disease. In this study, we assess just the following symptoms: age, sex, chest pain type range of 1–5, serum cholesterol, maximum heart rate attained, overnight sugar levels range of 0 or 1, and resting electrocardiogram range of 0–2, ST depression brought on by activity compared to rest, ST section for the peak reps, exercise-induced angina, by using fluoroscopy and Thal, the main vessels’ number (0–3) were colored. The Cleveland cardiovascular database from the UCI repository is one of the datasets which is used in the present study, then applying a machine learning approach and classifying whether it is affected or not. We are using Ridge Classifier, Linear Discriminant Analysis, Extra Trees Classifier, Naive Bayes, and Logistic Regression Model. Finally, comparison of different machine learning-based available methods using the same database with the performance of a proposed method for the detection of heart disease has been done. Linear Discriminant Analysis has given accuracy (acc) and specificity (spec) that is 85.71% and 93.87%, respectively. But in the case of sensitivity (sen) of Ridge Classifier 83.33% which is best as compared with other classifier, overall, the Linear Discriminant Analysis gives better result as compared with other classifiers.
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Saini, S.K., Chandel, G. (2023). Effective Machine Learning-Based Heart Disease Prediction Model. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 787. Springer, Singapore. https://doi.org/10.1007/978-981-99-6550-2_14
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