A Systematic Approach of Classification Model Based Prediction of Metabolic Disease Using Optical Coherence Tomography Images

Abstract

Data mining is defined as the upcoming field that consists of certain tools and techniques to be implemented with certain data sets taken from the different sources to foresee the hidden information. The data mining is the huge upcoming field has attracted many fields under its influence. In the applications of data mining, health care is a very important application to be taken account. Healthcare is defined as the service provides the health maintenance and earlier disease prediction and also provides high quality treatments to prevent disease. Human body consists of a number of cells constituted to form organs and the organs connected to form the organ system. This system should be interconnected to work properly. The human body should be nourished properly by balanced diet and the healthy lifestyle. The function of the human body is disturbed by some external factors called disease. The metabolic disease is the collection of five different disorders such as high blood pressure, heart problems, obesity and insulin resistance. The Optical Coherence Tomography images of eyes are considered to predict the chronic conditions of the body accurately in the eyes. The main focus of this work is to detect diabetes through the retina images. This paper mainly reflects detection of diabetes using retina images. In this paper the classification techniques are analyzed using orange data mining tool to find the best classification technique based on the individual technique’s prediction accuracy.