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A hybrid machine learning approach for hypertension risk prediction

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Abstract

Hypertension is a primary or contributing cause for premature death in the entire world. As a matter of fact, there is a high prevalence and low control rates in low- and middle-income countries, such that the prevention and treatment of hypertension should remain a top priority in global health. In the recent years, the awareness, treatment, and control rates of hypertension patients in China have been significantly improved to 51.6%, 45.8%, and 16.8%, respectively. However, those rates are still far from a satisfactory level. Clinical studies suggest that for people in the pre-clinical stage of hypertension or having the risk of hypertension, the progression of the disease may be significanly reduced through a change in lifestyle, or by an effective drug therapy. In this paper, we address risk prediction for hypertension in the next five years, and put forward a model merging KNN and LightGBM. Our approach allows us to predict the hypertension risk for a specific individual using features such as the age of the subject and blood indicators. Results shows that our model is reliable and achieves accuracy and recall rate over 86% and 92%, respectively.

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Authors and Affiliations

  1. Education Center of Experiments and Innovations, Harbin Institute of Technology (ShenZhen), Shenzhen, 518055, China

    Min Fang, Rui Xue & Ting Su

  2. Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, 518051, China

    Yingru Chen

  3. Cybersecurity Program, St. Bonaventure University, New York, NY, 14778, USA

    Huihui Wang

  4. College of Computer Science and Engineering, Shenzhen University, Shenzhen, 518060, China

    Nilesh Chakraborty & Yuyan Dai

Authors
  1. Min Fang
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  2. Yingru Chen
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  3. Rui Xue
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  4. Huihui Wang
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  5. Nilesh Chakraborty
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  6. Ting Su
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  7. Yuyan Dai
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Correspondence to Min Fang.

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Fang, M., Chen, Y., Xue, R. et al. A hybrid machine learning approach for hypertension risk prediction. Neural Comput & Applic 35, 14487–14497 (2023). https://doi.org/10.1007/s00521-021-06060-0

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  • DOI: https://doi.org/10.1007/s00521-021-06060-0

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