Cardiovascular disease after post-COVID has become a life-threatening, deadly disease. A major percentage of the mortality rate occurring every year is due to heart-related diseases. India, being a middle-income nation, is facing a severe need for awareness and resources to reduce the untimely death, especially in the middle-aged population, due to cardiac arrest. Machine learning has been acting as an essential tool to predict an early occurrence of this fatal disease. There is a need to provide personalized recommendations to the patient if the patient is predicted to have heart disease. The paper aims at providing the various content-based recommendations to the patients based on 5 parameters: age, FBS, trestbps, chol, thalach, and CP so that the precautionary measures need to be taken by the person based on the recommendation provided by the model. The contribution of this work is summarized into three parts. a) A stacking ensemble-based meta-learner is developed to predict a person with heart disease. b) A machine learning pipeline is incorporated to automate the workflow of disease detection and c) A novel personalized recommendation method with respect to cardiovascular risk reduction and preventative measures providing the optimal solutions to the person suffering from heart disease and its prognosis. The proposed method is validated by providing the necessary recommendations to the patients, demonstrating significant risk reduction for individuals with high CVD risk. The result evaluation is done using a t-test showing a statistically significant level and an ROC curve with a value of 0.98 and a sensitivity analysis with a value of 0.91. The mean average precision (MAP) value is 0.75. Creating a machine learning-based model that forecasts the likelihood of heart disease onset is the aim of this research. The existence or absence of heart illness, given as a binary classification (0 = no heart disease, 1 = heart disease), is the outcome variable for this prediction task.

Cardiovascular disease (CVD) is a leading cause of death worldwide and hence requires early risk assessment and focused preventative measures. The study describes a novel two-phase hybrid approach that combines machine learning-based CVD risk prediction and personalized lifestyle advice. In the first phase, cardiovascular risk is estimated using ensemble classifier that combines Random Forest Classifier, SVM and LR using metal learner trained on the Heart Disease dataset (1000 record, 14 attributes) has excellent predictive accuracy. In the second phase, optimization framework produces lifestyle suggestions that are safe for health within clinically determined parameters, which are enhanced using a hybrid recommendation system that combines content-based and Cluster-based Outcome Analysis. The suggested approach considerably outperformed a baseline of general lifestyle recommendations in a simulated high-risk cohort, exhibiting an average relative risk reduction of [X] % over a 10-year period as determined by the Framingham Risk Score. The suggested approach is made to be validated in future research using external datasets, simulated patient trials, and physician evaluation in order to guarantee clinical relevance This methodology highlights the promise for precision cardiovascular prevention by providing personalized, data-driven lifestyle recommendations.