Key result
A multi-agent prediction model improves BP prediction accuracy over existing state-of-the-art methods.
Why the study?
Current hypertension prediction models rely on standard guidelines and batch learning methods, lacking personalized solutions and intelligent anomaly handling for reliable predictions.
Does an integrated multi-agent-based hypertension risk prediction system improve prediction accuracy compared to existing online prediction methods in a Malaysian population?
Does an integrated multi-agent-based hypertension risk prediction system improve prediction accuracy compared to existing online prediction methods in a Malaysian population?
A novel multi-agent machine learning model using OIESGP improves the accuracy of personalized 4-year hypertension risk prediction compared to existing methods.
Hypertension is a global health problem and a leading factor in severe and life-threatening cardiovascular diseases (CVD) and stroke. The onset is dependent on individual lifestyle choices, and no single root cause of the condition exists. Various machine learning solutions are proposed for the early diagnosis of hypertension and its prediction, but they are based on standard guidelines and do not provide personalized solutions. Current models mainly rely on batch learning methods and do not readily learn the new incoming data. There is also a lack of an intelligent technique for handling anomalies in data, which leads to unreliable prediction results. In this paper, an integrated multi-agent-based hypertension risk prediction system is proposed that detects and computes missing values in the time series and provides personalized hypertension risk predictions. The proposed solution incorporates Gaussian mixture models for enhancing the input data, and an Online Infinite Echo State Gaussian Process (OIESGP) is used to obtain real-time prediction distribution of blood pressure. The prediction system readily absorbs new incoming data, and the model is updated to learn any new patterns in the data. The hypertension risk score is estimated using the Framingham hypertension risk estimator, and a 4-year hypertension risk is computed. The prediction performance of the proposed model is evaluated on blood pressure data gathered from the Malaysian population using mean absolute error, mean square error, and root-mean-square errors. The experimental results indicate that the proposed prediction model exhibits greater prediction accuracy than existing state-of-the-art online prediction methods.
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Abrar et al. (2021) studied Hypertension. Multi-agent personalized hypertension risk prediction system (OIESGP) vs. Existing online and batch prediction methods (e.g., OR-ELM, FOS-ELM, LSTM, SVM) was evaluated on Prediction accuracy (MAE, MSE, RMSE). The proposed multi-agent prediction model using an Online Infinite Echo State Gaussian Process exhibited greater prediction accuracy for blood pressure than existing state-of-the-art online and batch prediction methods.
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