International Journal of Science, Engineering and TechnologyAuthors: Dhanush G, Stalin Rayappan S
Abstract: – Parkinson’s Disease (PD) is a progressive neurological disorder that mainly affects motor functions such as speech, movement, and coordination. Early detection plays a crucial role in improving patient care and slowing disease progression. In recent years, machine learning techniques using biomedical signals have shown promising results for non-invasive diagnosis. This paper presents a novel approach based on a Topological Data Analysis (TDA)-driven multi- resolution sinusoidal machine learning model for early detection of Parkinson’s Disease using voice, gait, and sensor data. The proposed system follows a complete data processing pipeline that includes data preprocessing, feature scaling, feature selection using mutual information, and dimensionality reduction using Principal Component Analysis (PCA). Classification is performed using Random Forest, Support Vector Machine (SVM), and Logistic Regression models. In addition to conventional features, the system integrates TDA to capture structural patterns in data and multi-resolution sinusoidal analysis to extract frequency-based characteristics from gait signals. Experimental analysis demonstrates that the combination of advanced feature extraction techniques with machine learning improves classification accuracy and robustness. Among the evaluated models, ensemble- based methods show better performance in handling complex biomedical data. The proposed approach provides a reliable, cost-effective, and non-invasive solution for early-stage Parkinson’s detection, making it suitable for real-world healthcare applications.
DOI: https://doi.org/10.5281/zenodo.20177653
