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Novel Data Mining Analysis Method on Risk Prediction of Type 2 Diabetes

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Abstract

Diabetes is the third chronic disease threatening human health after cardiovascular and cerebrovascular diseases and malignant tumors. The latest survey shows that there are as many as 463 million diabetic patients in the world, most of which are type 2 diabetes, and present a state of high incidence. Therefore, preventing and controlling the occurrence of type 2 diabetes is of great strategic significance for protecting human health and saving medical resources. This paper uses the SVM classification technology in data mining to establish a type 2 diabetes risk prediction model based on the SVM classifier, and uses the model to predict the original data of diabetic patients in the endocrinology department of a third-class hospital in Wuhan. Finally, an evaluation tool is used to evaluate the prediction performance and quality of the prediction model. The experimental results show that the prediction model based on the SVM classifier has the advantages of high prediction accuracy, good stability, fast learning speed and good classification effect under complex clinical data sets. It has important guiding significance for assisting the clinical diagnosis and risk prediction of type 2 diabetes.

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Hong Guo & ZhiChao Fan

  2. Intensive Care Unit, Wuhan third hospital, Wuhan, China

    Yan Zeng

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  1. Hong Guo
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  2. ZhiChao Fan
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  3. Yan Zeng
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Correspondence to Hong Guo.

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Guo, H., Fan, Z. & Zeng, Y. Novel Data Mining Analysis Method on Risk Prediction of Type 2 Diabetes. J Sign Process Syst 94, 1183–1198 (2022). https://doi.org/10.1007/s11265-021-01717-4

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  • DOI: https://doi.org/10.1007/s11265-021-01717-4

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