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Providing diagnosis on diabetes using cloud computing environment to the people living in rural areas of India

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Abstract

Diabetes is one of the major diseases prevalent today affecting around 400 million people worldwide. Approximately one in ten adult people worldwide have diabetes. Unfortunately, about half of them live in rural areas and are not aware about the severity of the disease. Treatment of diabetes is feasible, but also challenging and expensive. Our contribution is to develop a reference model in assisting rural people suffering from diabetes. It helps the rural people of India in characterizing the victims of diabetes 2 at the earlier stages. This model improves the communication and interaction between patients and doctors. The target of analysis made in the present research is to list the risks factors and correlation that exist among those risk factors. In this work, logistic regression, support vector machine, random forest, decision tree, Naive Bayes, K nearest neighbor classifiers are used for prediction, and their accuracy is compared to choose the better machine learning model. SVM provides higher accuracy (96.0) among the choosen algorithms.

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Funding

Funding was provided by European Cooperation in Science and Technology (Grant no. CA15222).

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Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Dharmendra Singh Rajput, Thippa Reddy Gadekallu, Rajesh Kaluri, Kuruva Lakshmanna & Praveen Kumar Reddy Maddikunta

  2. School of Computing and Information Technology, Reva University, Bangalore, India

    Syed Muzamil Basha

  3. Science and Technology, University of the Faroe Islands, Faroe Islands, Denmark

    Qin Xin

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  1. Dharmendra Singh Rajput
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  2. Syed Muzamil Basha
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  3. Qin Xin
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  4. Thippa Reddy Gadekallu
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  6. Kuruva Lakshmanna
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  7. Praveen Kumar Reddy Maddikunta
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Correspondence to Qin Xin.

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Rajput, D.S., Basha, S.M., Xin, Q. et al. Providing diagnosis on diabetes using cloud computing environment to the people living in rural areas of India. J Ambient Intell Human Comput 13, 2829–2840 (2022). https://doi.org/10.1007/s12652-021-03154-4

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