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ISSN 2063-5346
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REMOTE HEALTHCARE MONITORING METHODOLOGY FOR DIABETES PATIENTS USING LOGISTIC REGRESSION OVER DECISION TREE FOR IMPROVING PRECISION

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M Nishanth Naren, V. Karthick
» doi: 10.31838/ecb/2023.12.sa1.478

Abstract

Aim: The goal of this research is to self monitor in remote healthcare and help in diabetic prediction for diabetes patients. Materials and Methods: There are two groups in this study. The first group built a Logistic Regression technique, whereas the second group constructed a Decision Tree with 104 samples. The sample size for Logistic Regression is 52, with a Decision Tree (N = 52) and G power (value = 0.8) sampling strategy. Result: The accuracy of the Decision Tree Method has been enhanced to 86%, while the accuracy of the Logistic Regression algorithm has been shown as 79%. The mean accuracy detection is ±2SD and the significance value is 0.000 (p<0.05) which shows the hypothesis is correct and it is carried out using an independent sample T test. conclusion: The Decision Tree Approach's final result (86%) was determined to be much more accurate than the Logistic Regression algorithm (79%).

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