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Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering
In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes is one of the most common chronic diseases which has affected to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can be aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with Type-2 diabetes mellitus. The processing and statistical analysis we used are Scikit-Learn, and Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge Type-2 diabetes mellitus chronic diseases.
Healthcare Machine learning Artificial Neural Network (ANN) Diabetes type-2 Prediction K-means clustering Classification model
Md. Tahsir Ahmed Munna, Mirza Mohtashim Alam, Shaikh Muhammad Allayear, Kaushik Sarker, Sheikh Joly Ferdaus Ara
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