Project Title

Parkinson’s Disease Detection Using Machine Learning with Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Healthcare Analytics | Medical Diagnosis | Data Science

Project Level

Beginner to Intermediate

Project Objective

Parkinson’s Disease is a progressive neurological disorder that affects movement, speech, and coordination. Early detection of Parkinson’s Disease can help healthcare professionals provide timely treatment and improve patients’ quality of life.

The objective of this project is to build an intelligent Machine Learning model capable of analyzing biomedical voice measurements and predicting whether a person is likely to have Parkinson’s Disease.

This project provides practical exposure to Artificial Intelligence, Healthcare Analytics, and Medical Decision Support Systems used in modern healthcare.

Session Access Link

Project Training Session

Session Link:

Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand disease detection using Machine Learning.
• Learn how healthcare prediction systems work.
• Analyze biomedical datasets.
• Perform data cleaning and preprocessing.
• Understand classification algorithms.
• Train and evaluate disease prediction models.
• Measure model performance using healthcare-related metrics.
• Build a real-world Parkinson’s Disease detection system.
• Improve analytical and problem-solving skills.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn

• NumPy

• Pandas

Data Visualization Libraries

• Matplotlib

• Seaborn

Development Platforms

• Google Colab

• Jupyter Notebook

• VS Code (Optional)

Data Storage

• CSV Files

• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Parkinson’s Disease Dataset containing:

• MDVP:Fo(Hz) (Average Vocal Frequency)
• MDVP:Fhi(Hz)
• MDVP:Flo(Hz)
• Jitter (%)
• Shimmer
• NHR (Noise-to-Harmonic Ratio)
• HNR (Harmonic-to-Noise Ratio)
• RPDE
• DFA
• PPE
• Status (Healthy / Parkinson’s Disease)

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Healthy vs Parkinson’s Distribution
• Correlation Analysis
• Feature Importance Analysis
• Histograms
• Box Plots
• Heatmaps
• Voice Measurement Analysis

Benefits:

• Understand differences between healthy and affected patients.
• Identify important medical indicators.
• Detect trends and anomalies in healthcare data.

Phase 4: Feature Engineering

Students will prepare data for machine learning using:

• Feature Scaling
• Standardization
• Correlation-Based Feature Selection
• Dimensionality Reduction (Optional)

Benefits:

• Improves model performance.
• Enhances disease detection accuracy.
• Reduces redundant medical features.

Phase 5: Model Building

Students will train Machine Learning models such as:

Support Vector Machine (SVM)

• Highly effective for medical diagnosis datasets.
• Provides strong classification performance.

Random Forest Classifier

• Ensemble learning technique.
• Improves prediction accuracy.

Logistic Regression

• Fast and interpretable model.
• Suitable for binary classification.

K-Nearest Neighbors (KNN)

• Simple and effective classification algorithm.
• Useful for model comparison.

Phase 6: Model Evaluation

Students will evaluate model performance using:

• Accuracy Score
• Confusion Matrix
• Precision
• Recall
• F1-Score
• ROC-AUC Score

Expected Accuracy:

85% – 98% (depending on dataset quality and model selection)

Phase 7: Disease Detection System

Students will create a system where users can:

• Enter biomedical voice measurements.
• Click Predict.
• Receive output:
  • Parkinson’s Disease Detected
  • No Parkinson’s Disease Detected

Example:

Input:

• Average Vocal Frequency
• Jitter
• Shimmer
• NHR

Output:

• Parkinson’s Disease Detected

or

• Healthy Individual

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)

• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

• Project Report (PDF)

Screenshots

• Dataset Import

• Data Cleaning

• Exploratory Data Analysis

• Model Training

• Evaluation Results

• Prediction Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Model (.pkl file)

• Prediction Results

• Healthcare Analysis Report

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

ParkinsonDiseaseDetection_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.