Cardiovascular disease has a significant global impact. Cardiovascular
disease is the primary cause of disability and mortality in most developed countries.
Cardiovascular disease is a condition that disturbs the structures and functionality of
the heart and can also be called heart disease. Cardiovascular diseases require more
precise, accurate, and reliable detection and forecasting because even a small
inaccuracy might lead to fatigue or mortality. There are very few death occurrences
related to cardio sickness, and the amount is expanding rapidly. Predicting this disease
at its early stage can be done by employing Machine Learning (ML) algorithms, which
may help reduce the number of deaths. Data pre-processing can be employed here to
eliminate randomness in data, replace missing data, fill in default values if appropriate,
and categorize features for forecasting and making decisions at various levels. This
research investigates various parameters that are related to the cause of heart disease.
Several models discussed here will come under the supervised learning type of
algorithms like Support Vector Machine (SVM), K-nearest neighbor (KNN), and Naïve
Bayes (NB) algorithm. The existing dataset of heart disease patients from the Kaggle
has been used for the analysis. The dataset includes 300 instances and 13 parameters
and 1 label are used for prediction and testing the performance of various algorithms.
Predicting the likelihood that a given patient will be affected by the cardiac disease is the goal of this research. The most important purpose of the study is to make better
efficiency and precision for the detection of cardiovascular disease in which the target
output ultimately matters whether or not a person has heart disease.
Keywords: Bayes, Evaluation Metrics, Prediction, Pre-process, Supervised.