Machine Learning and Ensemble Approach Onto Predicting Heart Disease
This work addresses the need for quick and correct diagnosis of cardiovascular disease, which is a leading cause of death, but it appears incremental as it applies standard machine learning methods without novel breakthroughs.
The paper tackled the problem of predicting heart disease by training multiple classification models and using a soft voting ensemble technique to improve diagnostic accuracy, but no concrete performance numbers were provided in the abstract.
The four essential chambers of one's heart that lie in the thoracic cavity are crucial for one's survival, yet ironically prove to be the most vulnerable. Cardiovascular disease (CVD) also commonly referred to as heart disease has steadily grown to the leading cause of death amongst humans over the past few decades. Taking this concerning statistic into consideration, it is evident that patients suffering from CVDs need a quick and correct diagnosis in order to facilitate early treatment to lessen the chances of fatality. This paper attempts to utilize the data provided to train classification models such as Logistic Regression, K Nearest Neighbors, Support Vector Machine, Decision Tree, Gaussian Naive Bayes, Random Forest, and Multi-Layer Perceptron (Artificial Neural Network) and eventually using a soft voting ensemble technique in order to attain as many correct diagnoses as possible.