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This final year project utilizes machine learning to detect Parkinson's Disease (PD) by analyzing kinematic features of hand movements, captured using Leap Motion sensors. Data from 16 PD patients and 16 control group individuals were recorded during three motor tasks: finger tapping (FT), pronation-supination (PS), and opening-closing hand movements (OC). A custom application extracted 25 kinematic features, with key points identified using maximums and minimums algorithms and manual marking. Four classifiers—kNN, SVM, Decision Tree (DT), and Random Forest (RF)—were trained using 8-fold cross-validation, achieving excellent performance with combined features from both hands. This IEEE-based project is ideal for computer science students seeking innovative healthcare solutions.
Upon purchasing this project online, you will receive recorded video tutorials, comprehensive documentation, complete frontend (HTML, CSS, JavaScript) and backend (Python with kNN, SVM, DT, RF implementations) codes, reports, PPTs, and datasets, all sent automatically to your email. For further support, contact our technical team at +91 8088605682. Please note that once payment is made, no refunds will be issued under any circumstances.
Receive fully functional and tested code for Parkinson's disease detection, implemented using Python with kNN, SVM, Decision Tree, and Random Forest algorithms, including a custom application for feature extraction.
Get detailed documentation, including reports, PPTs, and datasets for research papers, sent automatically to your email upon purchase.
For personalized guidance on installation and implementation, contact our technical team at +91 8088605682 to arrange one-to-one online sessions (additional charges apply).
For project customization or additional feature integration, contact our technical team at +91 8088605682 to discuss your requirements (additional charges apply).
For hands-on, in-person assistance, contact our team at +91 8088605682 to arrange offline support at our Bangalore center (additional charges apply).
This is one of the best IEEE Machine Learning project ideas for final-year students. The project includes complete frontend (HTML, CSS, JavaScript) and backend (Python with kNN, SVM, DT, RF implementations) codes, along with detailed explanations to ensure thorough understanding. Smart AI Technologies offers thorough guidance, complete support in implementing the Parkinson's disease detection system, and content for your report and IEEE paper publication, making it a commercially viable solution for applications in healthcare diagnostics and beyond.