A Multi-Agent Approach for Personalized Hypertension Risk Prediction

Abstract

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 [5] focused on hypertension as a problem for health since it can cause several chronic heart conditions, including stroke, renal failure, and cardiac arrest. Multiple risk factors, including a person's lifestyle and genetics, can result in hypertension.…”

“…This research introduced a multi-agent system that can compute missing values concerning time and provide personalized prediction of the risk of hypertension. Abrar et al [5] obtained realtime blood pressure readings using a Gaussian process model. The new input data are also learned by this model.…”

“…This vast amount of data is processed via ML to identify patterns, anomalies, and trends, assisting in personalized diagnosis and treatment plans. It can predict disease conditions and evaluate factors, on the basis of which risk assessments can be made for personalized interventions [5]. ML algorithms heavily rely on the quality and quantity of data.…”