Abstract
Parkinson’s disease (PD) remains a significant challenge in medical diagnosis and monitoring, requiring robust machine learning techniques to enhance predictive accuracy and improve patient outcomes. This study explores the use of Logistic Regression, ensemble learning models, and deep learning LSTM model to classify PD symptoms more effectively and reliably. Additionally, feature engineering techniques such as velocity, acceleration, sliding window, rolling mean, rolling variance, and time difference are incorporated to enhance model performance, capture essential movement patterns, and mitigate data noise. Experimental results demonstrate that feature engineering significantly improves classification accuracy, with Decision Tree achieving the highest testing accuracy of 0.989, Other tree models such as XGBoost and Random forest also give good result in accuration of 0.979 and 0.961 when combined using sliding window. Furthermore, the integration of deep recurrent architectures enhances the detection of temporal patterns in PD tremors, with LSTM achieving a testing accuracy of 0.73. These findings highlight the effectiveness of combining ensemble learning with deep learning for PD detection, offering potential improvements for early diagnosis, continuous patient monitoring, personalized treatment strategies, and disease progression assessment.
https://ror.org/0004wsx81 
