# Early Prediction of Heart Disease Using PCA and Hybrid Genetic Algorithm with k-Means

arXiv: https://arxiv.org/abs/2101.00183 · HTML: https://mlsanity.com/paper/2101.00183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel Hybrid Feature-selection Method and Robust Data Preparation Pipeline for Different ML Techniques](https://mlsanity.com/paper/2203.01394/) (2022)
- [Automated Detection of Acute Lymphoblastic Leukemia Subtypes from Microscopic Blood Smear Images using Deep Neural Networks](https://mlsanity.com/paper/2208.08992/) (2022)
- [An Improved Heart Disease Prediction Using Stacked Ensemble Method](https://mlsanity.com/paper/2304.06015/) (2023)
- [Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images](https://mlsanity.com/paper/2310.13108/) (2023)
- [An experimental study for early diagnosing Parkinson's disease using machine learning](https://mlsanity.com/paper/2310.13654/) (2023)

## Abstract (arXiv)

Worldwide research shows that millions of lives lost per year because of heart disease. The healthcare sector produces massive volumes of data on heart disease that are sadly not used to locate secret knowledge for successful decision making. One of the most important aspects at this moment is detecting heart disease at an early stage. Researchers have applied distinct techniques to the UCI Machine Learning heart disease dataset. Many researchers have tried to apply some complex techniques to this dataset, where detailed studies are still missing. In this paper, Principal Component Analysis (PCA) has been used to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means used for final clustering. Typically, the k-means method is using for clustering the data. This type of clustering can get stuck in the local optima because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA) for data clustering to avoid this problem. Our proposed method can predict early heart disease with an accuracy of 94.06%.