Key result
A proposed machine learning method combining Principal Component Analysis and a Hybrid Genetic Algorithm with k-Means predicted early heart disease with an accuracy of 94.06%.
Why the study?
Millions of lives are lost annually to heart disease, requiring early detection, but detailed studies applying techniques to extract knowledge from the UCI Machine Learning heart disease dataset are still missing.
A hybrid machine learning approach combining PCA and a genetic algorithm with k-means clustering achieved 94.06% accuracy in predicting heart disease using the UCI dataset.
Should not yet change practice; leaves open prospective validation of this hybrid ML approach.
Worldwide research shows that millions of lives lost per year because of heart disease. The healthcare sector produces massive volumes of data on heart disease that are sadly not used to locate secret knowledge for successful decision making. One of the most important aspects at this moment is detecting heart disease at an early stage. Researchers have applied distinct techniques to the UCI Machine Learning heart disease dataset. Many researchers have tried to apply some complex techniques to this dataset, where detailed studies are still missing. In this paper, Principal Component Analysis (PCA) has been used to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means used for final clustering. Typically, the k-means method is using for clustering the data. This type of clustering can get stuck in the local optima because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA) for data clustering to avoid this problem. Our proposed method can predict early heart disease with an accuracy of 94.06%.
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Islam et al. (2020) studied Heart disease (n=303). PCA and Hybrid Genetic Algorithm with k-Means was evaluated on Clustering Accuracy. A proposed machine learning method combining Principal Component Analysis and a Hybrid Genetic Algorithm with k-Means predicted early heart disease with an accuracy of 94.06%.
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