Comparative Analysis of Machine Learning Algorithms for Heart Attack Classification: K-Nearest Neighbors vs. Naïve Bayes
Abstract
Cardiovascular diseases, including heart attacks, remain a significant global health concern. Early detection and accurate classification of individuals at risk are essential for effective prevention and intervention. In this research paper, we evaluate the performance of two popular machine learning algorithms, K-Nearest Neighbors (K-NN) and Naïve Bayes, for the classification of heart attack cases. Using a dataset comprising 1319 instances and 9 features, we compare the accuracy, precision, and recall of these algorithms.
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Introduction
Cardiovascular diseases, including heart attacks, represent a pervasive and critical global health concern, responsible for a significant portion of morbidity and mortality worldwide [1][4]. The timely identification and accurate classification of individuals at risk of heart attacks are vital for implementing preventive measures and providing appropriate medical interventions [10][11]. With the advent of machine learning and data analytics, there has been a growing interest in leveraging these technologies to aid in the early detection and classification of heart attack cases.
In this research paper, we delve into the realm of machine learning for healthcare by conducting a comparative analysis of two widely used algorithms: K-Nearest Neighbors (K-NN) and Naïve Bayes. Our study aims to assess the performance of these algorithms in classifying heart attack cases using a dataset specially curated for this purpose, known as the Heart Attack Classification dataset. With 1319 instances and nine relevant features, this dataset presents a challenging and realistic scenario for evaluating the algorithms' effectiveness in medical diagnosis.
The objective of this research is to provide insights into the capabilities of K-NN and Naïve Bayes in accurately identifying individuals who may be at risk of experiencing a heart attack. By measuring key metrics such as accuracy, precision, and recall, we aim to determine which algorithm exhibits superior performance in this critical task.
Conclusion
In this research paper, we compared the performance of K-NN and Naïve Bayes algorithms in classifying heart attack cases using the Heart Attack Classification dataset. Our findings suggest that Naïve Bayes outperforms K-NN in terms of accuracy, precision, and recall. These results highlight the potential of machine learning algorithms, particularly Naïve Bayes, in assisting healthcare professionals in identifying individuals at risk of heart attacks.
Further research can explore the incorporation of additional features or more advanced machine learning techniques to improve classification accuracy and to account for potential data imbalances. Additionally, real-world validation and clinical studies are necessary to assess the practical applicability of these algorithms in a healthcare setting.