An Approach for Early Heart Attack Prediction Systems Using K-Means Clustering and Cosine Similarity
Keywords:K-Means Clustering, Cosine similarity, Heart attacks, Prediction system, Clinical characteristics
In this study, we used cosine similarity and k-means clustering to construct a system to predict heart attacks. In order to divide patient data into groups with distinct clinical profiles based on their clinical characteristics, the k-means clustering approach is used. The new patient profiles were also contrasted with predetermined risk group profiles using the cosine similarity method. Heart attack high-risk patients are those with a profile that resembles that of the high-risk category. This suggested prediction system offers numerous benefits and contributions. First, the technique helps identify individuals who are at high risk of having a heart attack, allowing for prompt intervention and treatment. Second, the technology aids in lowering the mortality and effects of a heart attack by foreseeing the possibility of one in high-risk patients. Combining the k-means clustering method and cosine similarity, this system can predict heart attacks with an accuracy and dependability of 93.71%. In order to aid medical practitioners in making wise decisions and enhancing patient care, this research offers fresh perspectives on how to understand and manage heart attacks.
Copyright (c) 2023 Fadhillah Azmi, Nanda Novita, Amir Saleh
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