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

TL;DR

This paper proposes a Parkinson's disease prediction model using Random Forest classification combined with PCA for feature reduction, achieving up to 90% accuracy, and compares it with PCA-ANN models.

Contribution

The study introduces a PCA-RF based prediction approach for Parkinson's disease and demonstrates its superior accuracy over PCA-ANN models.

Findings

Achieved up to 90% accuracy in Parkinson's disease prediction.

PCA-RF outperforms PCA-ANN in accuracy.

Highlights the effectiveness of combining PCA with Random Forest for disease prediction.

Abstract

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%.