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Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm

Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm

Nagaraj V. Dharwadkar, Shivananda R. Poojara, Anil K. Kannur
Copyright: © 2021 |Pages: 23
ISBN13: 9781799830535|ISBN10: 1799830535|EISBN13: 9781799830542
DOI: 10.4018/978-1-7998-3053-5.ch014
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MLA

Dharwadkar, Nagaraj V., et al. "Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm." Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, edited by Bhushan Patil and Manisha Vohra, IGI Global, 2021, pp. 307-329. https://doi.org/10.4018/978-1-7998-3053-5.ch014

APA

Dharwadkar, N. V., Poojara, S. R., & Kannur, A. K. (2021). Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm. In B. Patil & M. Vohra (Eds.), Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 307-329). IGI Global. https://doi.org/10.4018/978-1-7998-3053-5.ch014

Chicago

Dharwadkar, Nagaraj V., Shivananda R. Poojara, and Anil K. Kannur. "Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm." In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, edited by Bhushan Patil and Manisha Vohra, 307-329. Hershey, PA: IGI Global, 2021. https://doi.org/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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