Influence of Exercise in Diabetes Mellitus Prediction in Big Data Using Hadoop/Map Reduce Frame work
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-10 |
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| Year of Publication : 2019 | ||
| Authors : Praveenkumar K S, Dr. R Gunasundari | ||
| DOI : 10.14445/22312803/IJCTT-V67I10P114 |
MLA Style:Praveenkumar K S, Dr. R Gunasundari"Influence of Exercise in Diabetes Mellitus Prediction in Big Data Using Hadoop/Map Reduce Frame work" International Journal of Computer Trends and Technology 67.10 (2019):81-84.
APA Style Praveenkumar K S, Dr. R Gunasundari. Influence of Exercise in Diabetes Mellitus Prediction in Big Data Using Hadoop/Map Reduce Frame work, International Journal of Computer Trends and Technology, 67(10),81-84.
Abstract
Now a day’s disease prediction is a developing area of research in the healthcare. There are number of diseases are still cannot be predictable. There are four main types of disease: infectious diseases, deficiency diseases, hereditary diseases (including both genetic diseases and non-genetic hereditary diseases), and physiological diseases. In other words any disorder or malfunctioning of the body or mind that destroys good health can be called as a disease. The status of health of the body in a disease is said to be compromised. A disease can be caused due to a variety of reasons. Every disease has characteristic symptoms through which we can identify the types of diseases. Early prediction and proper treatments can possibly stop, or slow the progression of disease. In the proposed study we consider the disease Diabetes Mellitus (DM), predictions using Big Data Tools. Although many data mining techniques have been applied to assess the main causes of diabetes, but only few sets of clinical risk factors are considered. In this study, theproposed system that can efficiently discover the rules to predict the risk level of patients based on the given parameter about their health. Here we evaluate many factors Hereditary and genetics factors, Stress, Body Mass Index, Increased cholesterol level, High carbohydrate diet, Nutritional deficiency, Nature of Exercises, Tension and worries, High blood pressure, Insulin deficiency, Insulin resistance. Then we evaluate and compare this system using suitable rules and Map Reduce algorithm. The performance of the system is evaluated in terms of different parameter like rules used, classification accuracy, and classification error. By considering all these parameters, the system can predict diabetics in a great accuracy. This paper mainly discuss on the non-clinical parameter Exercise and its influence on diabetics.
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Keywords
Diabetes Mellitus; Big Data, Hadoop/Map Reduce, Clinical factors, Non-clinical factors