An Efficient Sequential Clustering Based Classification Model for Diabetes Diagnosis and Prediction

Abstract

At last decade, the development of diverse models and the excessive data creation leads to an enormous production of dataset and source. The healthcare field offers rich in information and it needs to be analyzed to identify the patterns present in the data. The commonly available massive amount of healthcare data characterizes a rich data field. The way of extracting the medical design is difficult because of the characteristics of healthcare data like massive, real, and complicated details. Various machine learning (ML) algorithms has developed to predict the existence of the diabetes disease. Due to the massive quantity of diabetes disease dataset, clustering techniques can be applied to group the data before classifying it. A new automated clustering based classification model is applied for the identification of diabetes. To cluster the healthcare data, sequential clustering (SC) model is applied. Then, logistic regression (LR) model is applied for the effective categorization of the clustered data. The experimentations have been directed by the benchmark dataset. The simulation outcomes demonstrate that the efficiency of the SC-LR method beats the prevailing methods to predict the diabetes diseases.