Abstract
Diabetes is an extremely common disease, caused due to increase in the level of sugar. Due to the high cost of Blood tests, it becomes an expensive treatment. The primary objective of this research is to identify a précised data model for the detection of diabetes. The major objectives for the research work can be defined as (i) Identification and examination of the most widely recognized data mining strategies used in present-day Decision Support Systems. (ii) Using data mining methods to find the level of danger of diabetes with the point of improving the nature of care. (iii) Identify an improved structure for diabetic visualization. The process involves KDD implementation for data processing and modeling. Five different data models were compared based on Accuracy and the equivalent ROC curve is plotted to find out the best method for data prediction. The model, which has the best outcome, had an accuracy of 88.56%.
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Srivastava, R., Kumar, S., Fore, V., Tomar, R. (2021). A Study of Five Models Based on Non-clinical Data for the Prediction of Diabetes Onset in Medically Under-Served Populations. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_12
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