  The point of care services and medication have become simpler with efficient
consumer electronics devices in a smart healthcare system. Cardiovascular
disease is a critical illness which causes heart failure, and early and prompt
identification can lessen damage and prevent premature mortality. Machine
learning has been used to predict cardiovascular disease (CVD) in the
literature. The article explains choosing the best classifier model for the
selected feature sets and the distinct feature sets selected using four feature
selection models. The paper compares seven classifiers using each of the
sixteen feature sets. Originally, the data had 56 attributes and 303
occurrences, of which 87 were in good health, and the remainder had
cardiovascular disease (CVD). Demographic data with several features make up
the four groups of overall features. Lasso, Tree-based algorithms, Chi-Square
and RFE have all been used to choose the four distinct feature sets, each
containing five, ten, fifteen, and twenty features, respectively. Seven
distinct classifiers have been trained and evaluated for each of the sixteen
feature sets. To determine the most effective blend of feature set and model, a
total of 112 models have been trained, tested, and their performance has been
compared. SVM classifier with fifteen chosen features is shown to be the best
in terms of overall accuracy. The healthcare data has been maintained in the
cloud and would be accessible to patients, caretakers, and healthcare providers
through integration with the Internet of Medical Things (IoMT) enabled smart
healthcare. Subsequently, the feature selection model chooses the most
appropriate feature for CVD prediction to calibrate the system, and the
proposed framework can be utilised to anticipate CVD.
