Which algorithms are used in this Parkinson's disease detection project?

The system implements and compares two ensemble algorithms: Random Forest and XGBoost. XGBoost achieved superior performance with 96.15% accuracy and 0.994 ROC-AUC.

What is the accuracy of the Parkinson's disease detection model?

The XGBoost model achieves 96.15% accuracy with an F1-Score of 96.20% and ROC-AUC of 99.41%, demonstrating excellent predictive capability for PD detection.

Who can benefit from this Parkinson's disease detection system?

Neurologists, primary care physicians, healthcare professionals, researchers, and patients can use this system for non-invasive screening, early detection, and clinical decision support.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Parkinson's Disease Detection Using Machine Learning

The Parkinson's Disease Detection Using Machine Learning system is a comprehensive machine learning solution that detects Parkinson's Disease using vocal feature analysis from the UCI Parkinson's dataset. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Random Forest and XGBoost with leak-free SMOTE, achieving 96.15% accuracy and 0.994 ROC-AUC.

The system utilizes the UCI Parkinson's Disease dataset containing 195 instances with 22 vocal features extracted from voice recordings of 31 individuals. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time Parkinson's detection, enabling non-invasive, cost-effective screening for healthcare professionals and researchers.

Python 3.8+ Machine Learning Random Forest XGBoost Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model PD Detection
XGBoost (96.15% Accuracy)
Random Forest (94.87% Accuracy)
Feature Engineering (jitter_composite, voice_quality_score)
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time PD Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

🌲 Random Forest

Ensemble learning with n_estimators=300, max_depth=12, class_weight='balanced'

🎯 Accuracy: 94.87%

⚡ XGBoost

Gradient boosting with n_estimators=250, max_depth=5, learning_rate=0.05, regularization

🎯 Accuracy: 96.15%

🔧 Feature Engineering

jitter_composite, shimmer_composite, voice_quality_score, nonlinear_complexity

📊 Top Feature: RPDE

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV Mean: 99.12%

Methodology & Workflow

1 Data Collection
195 voice samples with 22 vocal features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Random Forest and XGBoost with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time PD detection

Model Performance Comparison

Metric	Random Forest	XGBoost	Best
Test Accuracy	94.87%	96.15%	XGBoost
Test Precision	95.12%	96.38%	XGBoost
Test Recall	94.87%	96.15%	XGBoost
Test F1-Score	94.91%	96.20%	XGBoost
Test ROC-AUC	98.23%	99.41%	XGBoost
CV Mean ROC-AUC	96.45%	99.12%	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial UCI Parkinson's Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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