Optimized Machine Learning System for Heart Failure Prediction

Heart failure and cardiovascular diseases are two severe medical problem which needs immediate preventive mea sures and early recognition of risk factors. Cardiovascular Car diovascular cardiovascular cardiovascular cardiovascular cardio vascular risk. But, many of the existing prediction systems focus more on developing a risk stratification than providing patient specific recommendations tailored to their clinical conditions. In this paper, we propose an Enhanced Machine Learning System for heart failure as well as Personalised Health Recom mendation Engine. To discover an effective prediction model, the proposed system collects demographic, clinical and lifestyle related data, preprocess it, select the features and evaluates various machine learning algorithms. It includes a risk category, prediction probability, confidence data along with Heart Health Score in the output of the prediction. The main contribution of the proposed system is the Person alised Recommendation Engine which takes machine learning predictions and circadian rhythm parameters (blood pressure, cholesterol, BMI, diabetes status, smoking habits) with phys ical activity, sleep duration stress level and dietary habits as individual parameters. Instead of issuing the same prevention recommendations to every user in a specific risk group, it produces individualized preventive recommendations based on each user's health profile. Explainable Artificial Intelligence methods can be adopted to ascertain the factors that impact the prediction. All these services are brought together under one interac tive web-based system through the proposed framework. It is interactive, user-centric, user-centric, user-centric, user-centric, user-centric, and user-centric.