# PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification

arXiv: https://arxiv.org/abs/2203.11287 · HTML: https://mlsanity.com/paper/2203.11287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 68 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre](https://mlsanity.com/paper/2107.13502/) (2021)
- [A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions](https://mlsanity.com/paper/2202.11965/) (2022)
- [HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model](https://mlsanity.com/paper/2203.08143/) (2022)
- [TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage Prevention Model](https://mlsanity.com/paper/2203.05367/) (2022)
- [An Improved Approach for Prediction of Parkinson's Disease using Machine Learning Techniques](https://mlsanity.com/paper/1610.08250/) (2016)
- [MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day](https://mlsanity.com/paper/2203.05835/) (2022)

## Built on by (most cited first, 1 of 1)

- [A smart resource management mechanism with trust access control for cloud computing environment](https://mlsanity.com/paper/2212.05319/) (2022)

## Abstract (arXiv)

In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has become quite easy. However, the complex and the selection of an optimal machine learning technique for the given dataset greatly affects the accuracy of the model. A large amount of datasets exists globally but there is no effective use of it due to its unstructured format. Hence, a lot of different techniques are available to extract something useful for the real world to implement. Therefore, accuracy becomes a major metric in evaluating the model. In this paper, a disease prediction approach is proposed that implements a random forest classifier on Parkinson's disease. We compared the accuracy of this model with the Principal Component Analysis (PCA) applied Artificial Neural Network (ANN) model and captured a visible difference. The model secured a significant accuracy of up to 90%.