Abstract
With the rapid advancement of technology and the ever increasing amount of data in the healthcare domain, big data analytics has become a significant study area. Analyzing patterns in patient treatment for the early detection and diagnosis of diseases can improve overall healthcare quality. Machine learning has emerged as a promising technology for aiding clinicians in making accurate diagnosis decisions. In this paper, we aim to propose an approach through the feature selection technique and employing various ML algorithms such as GBDT, NB, K-NN, SVM, LR, RF, and DT that will identify the subset of features relevant to the prediction of diabetes disease. The performance of each algorithm is evaluated using the Pima Indians Diabetes Dataset and Korean National Health and Nutrition Dataset. Experimental results show that the GBDT algorithm performs the best in predicting the disease with the highest accuracy.
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Kang, Ia., Ngnamsie Njimbouom, S., Kim, JD. (2023). An Effective Feature Selection for Diabetes Prediction. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2023 Workshops. DEXA 2023. Communications in Computer and Information Science, vol 1872. Springer, Cham. https://doi.org/10.1007/978-3-031-39689-2_10
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