Parkinson’s disease Detection Using Tree Based Machine Learning Algorithms

Authors

  • Venkata Srinivas Babu Oguri 1Department of Computer Science and Engineering, Mahindra University, Bahadurpally Jeedimetla, Hyderabad - 500043 - Telangana, INDIA
  • Sudhakar Poda 2Department of Biotechnology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur-522510, Andhra Pradesh, India
  • A. Krishna Satya 2Department of Biotechnology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur-522510, Andhra Pradesh, India
  • NK Prasanna 3CSIR- National Institute of Science Communication & Policy Research, New Delhi-110 012, Delhi, India

DOI:

https://doi.org/10.5530/ctbp.2023.2.19

Keywords:

Parkinson’s Disease (PD), LightGBM, Pearson Correlation, Accuracy, Er-ror Rate, Jupyter Notebook

Abstract

Parkinson’s Disease (PD), also known as primary Parkinsonism is a persistent, idio-pathic, degenerative nervous disorder which re-sults from lack of dopaminergic neurons in the substantia nigra pars compacta, which is the source of nigrostriatal dopamine pathway with-in the midbrain. The clinical detection relies on motor symptoms recognition. Significant neu-rological damage is already done by the time motor symptom occur. Early detection is nec-essary to stalk the progression of the disease. The problem of detection of PD comes under classification. Several tree-based classification algorithms were applied to the dataset retrieved from UCI machine learning database. The data-set was first split into train and test data. Various models were created using four different algo-rithms. Correlation coefficients were calculat-ed for each of the features in the dataset. The model was fitted with train data obtained after removing highly correlated features. Predictions were made and various parameters were con-sidered for comparison. Accuracy, precision, recall, F1-Score, Youden Index, error rate and specificity were the parameters calculated. Out of the four algorithms (Decision Tree, Random Forest, XGBoost and LightGBM), LightGBM achieved the highest accuracy of 97.43%.

DecisionTree

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Published

22-05-2023

How to Cite

Oguri, V. S. B. ., Poda, S., Satya, A. K., & Prasanna, N. . (2023). Parkinson’s disease Detection Using Tree Based Machine Learning Algorithms. Current Trends in Biotechnology and Pharmacy, 17(2), 808–818. https://doi.org/10.5530/ctbp.2023.2.19