# Analyzing the impact of feature selection on the accuracy of heart disease prediction

arXiv: https://arxiv.org/abs/2206.03239 · HTML: https://mlsanity.com/paper/2206.03239/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Nighttime sky/cloud image segmentation](https://mlsanity.com/paper/1705.10583/) (2017)
- [A predictive analytics approach for stroke prediction using machine learning and neural networks](https://mlsanity.com/paper/2203.00497/) (2022)
- [Validating Clustering Frameworks for Electric Load Demand Profiles](https://mlsanity.com/paper/2103.00030/) (2021)
- [Identifying Stroke Indicators Using Rough Sets](https://mlsanity.com/paper/2110.10152/) (2021)

## Built on by (most cited first, 3 of 3)

- [Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance](https://mlsanity.com/paper/2411.00851/) (2024)
- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [A data balancing approach towards design of an expert system for Heart Disease Prediction](https://mlsanity.com/paper/2407.18606/) (2024)

## Abstract (arXiv)

Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade. In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor. Recently we have seen the usage of non-invasive medical procedures, such as artificial intelligence-based techniques in the field of medical. Specially machine learning employs several algorithms and techniques that are widely used and are highly useful in accurately diagnosing the heart disease with less amount of time. However, the prediction of heart disease is not an easy task. The increasing size of medical datasets has made it a complicated task for practitioners to understand the complex feature relations and make disease predictions. Accordingly, the aim of this research is to identify the most important risk-factors from a highly dimensional dataset which helps in the accurate classification of heart disease with less complications. For a broader analysis, we have used two heart disease datasets with various medical features. The classification results of the benchmarked models proved that there is a high impact of relevant features on the classification accuracy. Even with a reduced number of features, the performance of the classification models improved significantly with a reduced training time as compared with models trained on full feature set.