# Machine Learning and Ensemble Approach Onto Predicting Heart Disease

arXiv: https://arxiv.org/abs/2111.08667 · HTML: https://mlsanity.com/paper/2111.08667/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification](https://mlsanity.com/paper/2001.09636/) (2020)

## Abstract (arXiv)

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.