Key result
A proposed automated machine learning system predicts heart disease risk using physical and medical datasets and provides personalized daily guidance, meal planning, and exercise scheduling.
Why the study?
Heart diseases are a leading cause of death in Sri Lanka, creating a need for an automated system to improve medical efficiency and identify disease early for proper treatment.
A proposed machine learning system aims to predict heart disease risk and provide personalized lifestyle interventions to improve patient outcomes.
Proposed ML tool for CVD risk prediction and guidance; hypothesis-generating and requires prospective validation before any clinical use.
Human heart is the principal part of the human body. Change in human lifestyle, work related stress and unhealthy food habits contribute to the increase in rate of numerous heart related diseases. In accordance with several research, various heart diseases have been the key reason for deaths in Sri Lanka. According to the 2018 records, stroke affected 31%, coronary heart disease affected 23%, and ischemic heart disease affected 14%. Therefore, there is a need for an automated system which will enhance medical efficiency and to identify such diseases in time for proper treatment. The proposed system takes physical and medical datasets of heart patients as manual input parameters and predicts the patient’s risk of having a heart disease. Prediction process grants the patient a risk level according to the heart condition and proposes a personalized daily guidance for the patient to avoid risks associated with, along with a meal planner, exercise scheduler and a stress releaser as well as alert the patient well in advance. The system will present an efficient technique of predicting heart diseases using machine learning approaches to analyze huge complex medical data. Some of the used algorithms are Random Forest, Logistic regression, Decision tree classifier etc... The research mainly aims to prevent the escalation of heart diseases in patients and lead them to a healthy lifestyle.
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Bandara et al. (2022) studied Heart disease. Heart Risk Prediction System was evaluated. A proposed automated machine learning system predicts heart disease risk using physical and medical datasets and provides personalized daily guidance, meal planning, and exercise scheduling.
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