• Md. SRINIVASULU M
• REETHU S
• SAHANA MARIGOOLAPPALAVAR
• SOWMYA S MASHAL
• RAMYA R

Parkinson's disease is a neurodegenerative disorder that affects millions of people worldwide. Early diagnosis and prediction of Parkinson's disease can significantly improve patient outcomes by enabling timely intervention and treatment. In recent years, machine learning techniques have emerged as promising tools for predicting Parkinson's disease based on various clinical and demographic factors. This abstract presents a study that explores the use of Python and machine learning algorithms for Parkinson's disease prediction. The dataset used in this study comprises a collection of anonymized patient records, including demographic information, medical history, and motor symptom assessments. Feature engineering techniques are applied to preprocess the data, including handling missing values, normalization, and feature selection. Several popular machine learning algorithms, such as logistic regression, support vector machines, random forests, and neural networks, are implemented and evaluated. Cross-validation and performance metrics such as accuracy, precision, recall, and F1-score are used to assess the models' predictive performance. The study employs various evaluation strategies, including training and testing on different subsets of the dataset, to ensure robustness and generalizability of the models. Additionally, different feature combinations are explored to identify the most influential predictors of Parkinson's disease.

Parkinson’s disease, prediction, machine learning, sci-kit learn, tensor flow

"A Study and Analysis on Parkinson’s disease Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.h494-h499, July-2023, Available :http://www.jetir.org/papers/JETIR2307762.pdf

"A Study and Analysis on Parkinson’s disease Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. pph494-h499, July-2023, Available at : http://www.jetir.org/papers/JETIR2307762.pdf