F. Ahmed Shaban; S. Golshannavaz. "A Machine-Learning-based Predictive Smart Healthcare System". فناوری آموزش, 13, 1, 1403, 181-188. doi: 10.22061/jecei.2024.11092.765
Shaban, F., Golshannavaz, S.. (1403). 'A Machine-Learning-based Predictive Smart Healthcare System', فناوری آموزش, 13(1), pp. 181-188. doi: 10.22061/jecei.2024.11092.765
Electrical Engineering Department, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.
تاریخ دریافت: 01 مرداد 1403،
تاریخ بازنگری: 25 مهر 1403،
تاریخ پذیرش: 10 آبان 1403
چکیده
Background and Objectives: In smart grid paradigm, there exist many versatile applications to be fostered such as smart home, smart buildings, smart hospitals, and so on. Smart hospitals, wherein patients are the possible consumers, are one of the recent interests within this paradigm. The Internet of Things (IoT) technology has provided a unique platform for healthcare system realization through which the patients’ health-based data is provided and analyzed to launch a continuous patient monitoring and; hence, greatly improving healthcare systems. Methods: Predictive machine learning techniques are fostered to classify health conditions of individuals. The patients’ data is provided from IoT devices and electrocardiogram (ECG) data. Then, efficient data pre-processings are conducted, including data cleaning, feature engineering, ECG signal processing, and class balancing. Artificial intelligence (AI) is deployed to provide a system to learn and automate processes. Five machine learning algorithms, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), logistic regression, Naive Bayes, and random forest, as the AI engines, are considered to classify health status based on biometric and ECG data. Then, the output would be the most proper signals propagated to doctors’ and nurses’ receivers in regard of the patients providing them by initial pre-judgments for final decisions. Results: Through the conducted analysis, it is shown that logistic regression outperforms the other AI machine learning algorithms with an F1 score, recall, precision, and accuracy of 0.91, followed by XGBoost with 0.88 across all metrics. SVM and Naive Bayes both achieved 0.85 accuracy, while random forest attained 0.86. Moreover, the Receiver Operating Characteristic Area Under Curve (ROC-AUC) scores confirm the robustness of Logistic Regression and XGBoost as apt candidates in learning the developed healthcare system. Conclusion: The conducted study concludes a promising potential of AI-based machine learning algorithms in devising predictive healthcare systems capable of initial diagnosis and preliminary decision makings to be relied upon by the clinician. What is more, the availability of biometric data and the features of the proposed system significantly contributed to primary care assessments.
