Open AccessPrognosis of Diabetes Mellitus using Machine Learning Techniques
Author(s) -
J Vidya,
Swastika T Jain,
Shyamala Boosi,
H C Bhanujyothi,
Dr.Chetana Tukkoji
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i5.1491
Subject(s) - diabetes mellitus , naive bayes classifier , medicine , disease , machine learning , support vector machine , blindness , random forest , artificial intelligence , boosting (machine learning) , computer science , intensive care medicine , optometry , endocrinology
Diabetes mellitus is a condition caused due to increase in blood glucose level. More than 90% of people are diagnosed with Type 2 diabetes disease,T2D is a fast-growing, chronic disease caused by the imbalance in insulin function. Diabetes is a now the leading cause of heart disease, stroke, blindness, non-traumatic limb amputations and end-stage renal failure. Early detection may take a step towards keeping diabetes patients healthy and it also reduces the risk of such serious complications. Nowadays, the application of Machine learning in the medical field is gradually increasing. This can aid in improving the classification system used for disease diagnosis, that assist medical experts in detecting the fatal diseases at an early stage. This paper presents a performance comparison of the machine learning algorithms in diabetes detection. Techniques like SVM, Random forest, Gradient Boosting, Navie Bayes, Logistic regressionand KNN are used in this work.