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Patient Diabetes Forecasting Based on Machine Learning Approach

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1154))

Abstract

In current scenario, machine learning plays an important role for forecasting diseases. The patient should passes through number of tests for diseases detection. This paper deals with the forecast of diabetes. The main idea is to predict the diabetic cases and find the factors responsible for diabetics using classification method. In this paper, an attempt has been made to integrating cluster and classification, which will gives a capable categorization result with highest accuracy rate in diabetes prediction using medical data with machine learning algorithms (such as logistic regression algorithms) and methods.

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Author information

Authors and Affiliations

  1. IFTM University, Moradabad, India

    Arvind Kumar Shukla

Authors
  1. Arvind Kumar Shukla
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Corresponding author

Correspondence to Arvind Kumar Shukla .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, IIT Roorkee, Roorkee, India

    Millie Pant

  2. Graphic Era Hill University, Dehradun, Uttarakhand, India

    Tarun Kumar Sharma

  3. NIT Patna, Patna, India

    Rajeev Arya

  4. NIT Patna, Patna, India

    B.C. Sahana

  5. Ryerson University, Toronto, ON, Canada

    Hossein Zolfagharinia

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Shukla, A.K. (2020). Patient Diabetes Forecasting Based on Machine Learning Approach. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_91

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