KNN在心臟病預測中表現最佳，準確率達96.7%。
Classification and Prediction of Heart Diseases using Machine Learning Algorithms
- 使用KNN算法對心臟病數據進行分類，僅需距離計算即可決策。
- 在UCI心臟病數據集上，KNN準確率達96.7%，超越其他模型。
- 適合醫療初篩與資源有限環境下的快速風險評估應用。
心臟病是全球範圍內嚴重的健康問題，因早期診斷不足導致大量死亡。心血管疾病是世界主要死因，因此建立可靠、高效且精確的心臟病預測系統是當前醫學界的重大挑戰。儘管已有相關工具，但多數成本高昂或難以應用。本研究旨在尋找最有效的機器學習算法來預測心臟病。實驗比較了邏輯回歸、K近鄰（K-Nearest Neighbor）、支持向量機及人工神經網絡等多種算法，基於著名的UCI心臟病資料庫進行評估。結果顯示，K近鄰算法在預測準確性上表現最佳，達到96.7%的正確率，顯著優於其他方法。未來可進一步探索更多機器學習模型在該領域的應用潛力。
原文摘要 · Abstract (English)
Heart disease is a serious worldwide health issue because it claims the lives of many people who might have been treated if the disease had been identified earlier. The leading cause of death in the world is cardiovascular disease, usually referred to as heart disease. Creating reliable, effective, and precise predictions for these diseases is one of the biggest issues facing the medical world today. Although there are tools for predicting heart diseases, they are either expensive or challenging to apply for determining a patient's risk. The best classifier for foretelling and spotting heart disease was the aim of this research. This experiment examined a range of machine learning approaches, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks, to determine which machine learning algorithm was most effective at predicting heart diseases. One of the most often utilized data sets for this purpose, the UCI heart disease repository provided the data set for this study. The K-Nearest Neighbor technique was shown to be the most effective machine learning algorithm for determining whether a patient has heart disease. It will be beneficial to conduct further studies on the application of additional machine learning algorithms for heart disease prediction.
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