An Analysis of Heart Disease Prediction using Machine Learning and Deep Learning Techniques

The significance of the heart as the body's most vital organ cannot be stressed. Heart disease is the leading cause of death worldwide. Heart failure (HF) is a main cause of death that must be successfully predicted (HF). Angiography, the gold standard for clinical diagnosis of HF, is expensive and can have catastrophic repercussions, according to research. In this scenario, machine learning and deep learning are applied. Machine learning and deep learning techniques can be used to forecast the whole range of hazards associated with this project. This dataset is created by combining previously available datasets. For your convenience, they are sorted into eleven distinct categories. This investigation would not be possible without this information. According to the findings, machine learning approaches exceeded deep learning in the diagnosis of cardiovascular diseases. PCA approach has been utilized to estimate the relative relevance of each of the dataset's 11 fields. When sample approaches are applied, accuracy and recall rates increased. According to the data, Random Forest Classifiers, Decision Tree Classifiers, and Naive Bayes algorithms surpass other MI algorithms.