Smart Healthcare Systems Using Predictive Machine Learning

  • Authors

    • Prof. Chinedu Okafor Dowen College Lagos, Nigeria. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2023PII2Q32

    Published 10-05-2023

  • Smart Healthcare, Predictive Machine Learning, Electronic Health Records, Wearable Sensors, Disease Prediction, Clinical Decision Support

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    C. Okafor, “Smart Healthcare Systems Using Predictive Machine Learning”, IJAIDT, vol. 6, no. 2, pp. 01–13, Oct. 2023, doi: 10.67228/30713315/IJAIDT-2023PII2Q32.

  • Abstract

    Smart healthcare systems can be viewed as a paradigm shift in the contemporary medical practice, since modern placement of sensing technologies, electronic health records (EHRs), and predictive machine learning (ML) technologies have become a common practice that facilitates proactive, personal, and efficient healthcare delivery. Such trends as the growing rate of chronic illnesses, the aging trend, and escalating healthcare costs have led to a desperate demand of smart systems able to diagnose and predict risks early, as well as make the optimal clinical decision. Predictive machine learning predictive models are models based on historical and real-time healthcare data that reveal the latent patterns, predict the disease progress, and assist clinicians to make evidence-based decisions. In this paper, predictive machine-learning has been adopted to analyze in detail the concept of smart healthcare systems, which involve system architecture, data acquisition, feature engineering, model development, and performance evaluation. The suggested framework combines the wearable sensor data, clinical records, and demographic information into a single analytics pipeline. Some of the machine learning algorithms under analysis and supervised or unsupervised, like logistic regression, support vector machines, random forests, gradient boosting, and deep neural networks, are discussed in the context of healthcare prediction tasks, such as disease risk prediction and hospital readmission prediction and patient outcome prediction. Moreover, the paper also touches on the issues associated with data quality, privacy, security, model interpretability and ethical issues. Recent experimental findings indicate that predictive ml-based healthcare systems are much better than conventional rule-based healthcare systems in terms of prediction accuracy and decision support. The results also show the promise of predictive machine learning to turn healthcare into either a reactive treatment or a preventive, personalized healthcare, as part of the vision of smart and sustainable healthcare ecosystems.

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