Open AccessDetection of Diabetic Patterns using Supervised Learning
Author(s) -
Kalpna Guleria,
Devendra Prasad,
Virender Kadyan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3473.129219
Subject(s) - random forest , machine learning , decision tree , artificial intelligence , naive bayes classifier , tree (set theory) , ensemble learning , supervised learning , computer science , diabetes mellitus , bayes' theorem , blindness , medicine , support vector machine , mathematics , bayesian probability , artificial neural network , optometry , mathematical analysis , endocrinology
World Health Organization’s (WHO) report 2018, on diabetes has reported that the number of diabetic cases has increased from one hundred eight million to four hundred twenty-two million from the year 1980. The fact sheet shows that there is a major increase in diabetic cases from 4.7% to 8.5% among adults (18 years of age). Major health hazards caused due to diabetes include kidney function failure, heart disease, blindness, stroke, and lower limb dismembering. This article applies supervised machine learning algorithms on the Pima Indian Diabetic dataset to explore various patterns of risks involved using predictive models. Predictive model construction is based upon supervised machine learning algorithms: Naïve Bayes, Decision Tree, Random Forest, Gradient Boosted Tree, and Tree Ensemble. Further, the analytical patterns about these predictive models have been presented based on various performance parameters which include accuracy, precision, recall, and F-measure.