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Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm
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Author(s): Nagaraj V. Dharwadkar (Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Sakhrale, India), Shivananda R. Poojara (Department of Computer Science and Engineering, University of Tartu, Estonia)and Anil K. Kannur (Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Sakhrale, India)
Copyright: 2021
Pages: 23
Source title:
Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
Source Author(s)/Editor(s): Bhushan Patil (Independent Researcher, India)and Manisha Vohra (Independent Researcher, India)
DOI: 10.4018/978-1-7998-3053-5.ch014
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Abstract
Diabetes is one of the four non-communicable diseases causing maximum deaths all over the world. The numbers of diabetes patients are increasing day by day. Machine learning techniques can help in early diagnosis of diabetes to overcome the influence of it. In this chapter, the authors proposed the system that imputes missing values present in diabetes dataset and parallel process diabetes data for the pattern discovery using Hadoop-MapReduce-based C4.5 machine learning algorithm. The system uses these patterns to classify the patient into diabetes and non-diabetes class and to predict risk levels associated with the patient. The two datasets, namely Pima Indian Diabetes Dataset (PIDD) and Local Diabetes Dataset (LDD), are used for the experimentation. The experimental results show that C4.5 classifier gives accuracy of 73.91% and 79.33% when applied on (PIDD) (LDD) respectively. The proposed system will provide an effective solution for early diagnosis of diabetes patients and their associated risk level so that the patients can take precaution and treatment at early stages of the disease.
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