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The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model

Author

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  • Zhiyuan Hao

    (School of Business and Management, Jilin University, Changchun 130012, China)

  • Jie Ma

    (School of Business and Management, Jilin University, Changchun 130012, China
    Information Resource Research Center, Jilin University, Changchun 130012, China)

  • Wenjing Sun

    (School of Business and Management, Jilin University, Changchun 130012, China)

Abstract

The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation–exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson’s disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2–7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers.

Suggested Citation

  • Zhiyuan Hao & Jie Ma & Wenjing Sun, 2022. "The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12509-:d:930582
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    1. Samuel Raafat Fahim & Hany M. Hasanien & Rania A. Turky & Abdulaziz Alkuhayli & Abdullrahman A. Al-Shamma’a & Abdullah M. Noman & Marcos Tostado-Véliz & Francisco Jurado, 2021. "Parameter Identification of Proton Exchange Membrane Fuel Cell Based on Hunger Games Search Algorithm," Energies, MDPI, vol. 14(16), pages 1-21, August.
    2. Xialv Lin & Xiaofeng Wang & Yuhan Wang & Xuejie Du & Lizhu Jin & Ming Wan & Hui Ge & Xu Yang, 2021. "Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model," IJERPH, MDPI, vol. 18(6), pages 1-25, March.
    3. Run-Hsin Lin & Chia-Chi Wang & Chun-Wei Tung, 2022. "A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers," IJERPH, MDPI, vol. 19(8), pages 1-9, April.
    4. Yanfeng Wang & Haohao Wang & Sanyi Li & Lidong Wang, 2022. "Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
    5. Meng Ji & Wenxiu Xie & Riliu Huang & Xiaobo Qian, 2021. "Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation," IJERPH, MDPI, vol. 18(18), pages 1-11, September.
    6. Hadeer Adel & Abdelghani Dahou & Alhassan Mabrouk & Mohamed Abd Elaziz & Mohammed Kayed & Ibrahim Mahmoud El-Henawy & Samah Alshathri & Abdelmgeid Amin Ali, 2022. "Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm," Mathematics, MDPI, vol. 10(3), pages 1-22, January.
    7. Mukkesh Kumar & Li Ting Ang & Hang Png & Maisie Ng & Karen Tan & See Ling Loy & Kok Hian Tan & Jerry Kok Yen Chan & Keith M. Godfrey & Shiao-yng Chan & Yap Seng Chong & Johan G. Eriksson & Mengling Fe, 2022. "Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus," IJERPH, MDPI, vol. 19(11), pages 1-17, June.
    8. Jiangnan Zhang & Kewen Xia & Ziping He & Zhixian Yin & Sijie Wang, 2021. "Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, February.
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