Parkinson’s Diseases Prediction and Comparison of Machine Learning Algorithms

Registration ID: IJNRD_192147

Published ID: IJNRD2304574

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Ujjwal Chandrakant Chaudhari, Mansi Ingle, Anchal Barbate, Palak Bhagat

Parkinson's disease, machine learning, prediction, XGBoost, Random Forest, KNN

Parkinson's disease (PD) is a neurological disorder that affects a significant number of people worldwide. Timely and accurate prediction of PD can help in early intervention and treatment, improving patient outcomes. While there is currently no known cure for the disease, early detection and treatment can reduce the cost of the disease and save lives. However, proper and timely detection of Parkinson's disease is challenging in underdeveloped countries due to limited resources and awareness. Additionally, symptoms vary among patients and may not all become apparent at the same stage of the disease.In this study, we investigate the application of machine learning techniques to predict PD using clinical data, with a focus on voice degradation as a symptom. We utilized various state-of-the-art machine learning algorithms, such as K-Nearest Neighbours (KNN), Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest Classifier, and XGBoost Classifier, to determine which algorithm is best suited for PD prediction. The performance evaluation parameters, including accuracy, precision, recall, F1 score, and Precision-Recall curve (PR curve), were used to compare the algorithms. We obtained the dataset for the study from the Oxford UCI Machine repository.Our study found that all four machine learning algorithms achieved high accuracy in predicting PD, with XGBoost achieving the highest accuracy of 96.61%, followed by Random Forest with 94.91%, KNN with 91.52%, and Decision Tree with 86.44%. Our study highlights the potential of machine learning techniques in accurately predicting PD using clinical data. The findings suggest that XGBoost, Random Forest, and KNN are effective tools for early PD prediction, providing valuable insights for clinical decision-making and personalized treatment planning.

Published Paper   E-Certificate

Ujjwal Chandrakant Chaudhari, Mansi Ingle, Anchal Barbate, & Palak Bhagat (April-2023). Parkinson’s Diseases Prediction and Comparison of Machine Learning Algorithms. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), f632-f637. https://ijnrd.org/papers/IJNRD2304574.pdf

Volume 8 Issue 4, April-2023

Pages : f632-f637

Paper Reg. ID: IJNRD_192147

Published Paper Id: IJNRD2304574

Downloads: 000122255

Research Area: Information Technology 

Author Type: Indian Author

Country: katol, maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304574.pdf

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