Machine Learning-based Web Application for Early Diagnosis of Diabetes

Hamayoun Yousaf Shahwani

Abstract


Diabetes has become a chronic disease that seriously threatens human health. It is a group of metabolic diseases characterized by hyperglycemia and there is no role of the age factor involved. The long-term of diabetes disease causes chronic damage and dysfunction of various tissues, especially the eyes, kidneys, heart, blood vessels, and nerves. Most of the time people are not sure about this common disease at the early stage and unluckily the patient moves to a critical situation to meet with major disease due to the continuous effect of diabetes. This research is conducted to build the machine learning-based web application platform for the early diagnosis of the disease, freely accessible anywhere anytime. We used the benchmark dataset named PIDD (Prima Indian Diabetes Dataset) and performed the comparative analysis among the Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest and Support Vector Machines. Based on the classification performance, we found that SVM performed the best among the pool of mentioned algorithms and, therefore, adopted for the development of the intelligent web application for the diabetes diagnosis.

Keywords


Classification, Support Vector Machine, Diabetes diagnosis, Diabetes prediction

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References


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DOI: http://dx.doi.org/10.36785/jaes.v10i2.418

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