Detection of Diabetic Patterns using Supervised Learning
Kalpna Guleria1, Devendra Prasad2, Virender Kadyan3
1Kalpna Guleria, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
2Devendra Prasad*, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
3Virender Kadyan, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1169-1173 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3473129219/2020©BEIESP | DOI: 10.35940/ijeat.B3473.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Machine Learning, Supervised Learning, Classification, Bio-informatics, Data Mining.