Multimodal Parkinson's Disease Detection using Random Forest
Overview
This repository provides a machine learning model for Parkinson's Disease detection using a multimodal approach that combines speech-based acoustic biomarkers and hand-drawn image features.
The model integrates clinically relevant voice features with Histogram of Oriented Gradients (HOG) extracted from spiral and wave drawings to improve diagnostic performance.
The classifier is a Grid Search optimized Random Forest model trained on fused multimodal features.
Parkinson's Disease is a progressive neurological disorder where early diagnosis is essential for effective treatment.
Traditional diagnosis often depends on clinical examination. This project demonstrates how machine learning can assist clinicians by analyzing multiple patient modalities simultaneously.
Model Details
Property
Value
Model
Random Forest Classifier
Optimization
Grid Search CV
Task
Binary Classification
Framework
Scikit-learn
Input
Voice + Drawing Features
Output
Healthy / Parkinson's Disease
Dataset
Voice Dataset
Source: UCI Parkinson's Dataset
Samples: 195
Parkinson's: 147
Healthy: 48
Drawing Dataset
Spiral and Wave Drawing Dataset
Total Images: 207
Training Images: 147
Testing Images: 60
Multimodal Dataset
Voice and drawing features were combined into a single feature vector after preprocessing and class balancing using Random Oversampling / SMOTE.
Input Features
Voice Features
Fundamental Frequency (Fo)
Highest Frequency (Fhi)
Lowest Frequency (Flo)
Jitter
Shimmer
NHR
HNR
RPDE
DFA
Drawing Features
Histogram of Oriented Gradients (HOG)
Preprocessing includes:
Grayscale conversion
Image resizing (250×250)
Otsu Thresholding
HOG Feature Extraction
Feature Fusion
The multimodal feature vector is generated by concatenating the processed voice features and HOG image descriptors.
