Heart disease can be prevented with accurate prediction, but it can also be fatal if the prediction
is erroneous. The results and characterization of the UCI Machine Learning Heart Disease
dataset are investigated in this research using various machine learning and deep learning
mechanisms. This study includes the ensemble of methods, well-known algorithms,
comparisons with other better methodologies, using an efficient feature selection technique,
hybrid approach, fuzzy-based algorithms, removing the noisy data using an enhanced
approach, and so on. The dataset contains 14 key attributes that were used in the assessment.
The precision of machine learning algorithms is determined by the dataset used for training
and testing. The knowledge saved can be useful as a source for anticipating future illnesses.
The purpose of this study is to summarize fresh research together with relative outcomes on
coronary health risk, as well as to encourage innovative goals using data mining and machine
learning frameworks.