What question did this study set out to answer?

The study aims to develop an efficient machine learning framework for the early detection of Parkinson's disease using biomedical data.

May 18, 2026

An Efficient Intelligent Machine Learning Framework for Early Parkinson's Disease Detection

Key Points

The study aims to develop an efficient machine learning framework for the early detection of Parkinson's disease using biomedical data.
Utilized supervised classification algorithms like K-NN and Naive Bayes for analysis.
Data was preprocessed and features scaled for model training.
Model performance evaluated using accuracy, precision, recall, and F1 scores.
K-NN and Naive Bayes achieved over 97% accuracy in detecting Parkinson's disease from acoustic signals.
The machine learning framework demonstrated superior performance compared to conventional methods.

Abstract

ABSTRACT Healthcare costs and motor and non‐motor functions are affected by Parkinson's disease (PD) worldwide. Detecting Parkinson's disease early allows for timely intervention, effective treatment planning, and slowing the disease. Recent advances in machine learning have made it possible to improve early diagnosis, especially when it comes to biomedical data, such as voice signals. Authors propose an intelligent machine learning framework for detecting Parkinson's disease at its earliest stage, based on supervised classification algorithms including LR, DT, RF, K‐NN, SVM, and NB. In the proposed framework, data is preprocessed, features are scaled, models are trained, and performance is evaluated by evaluating accuracy, precision, recall, and F1 scores. According to the results of our experiment, KNN and Nave Bayes perform better than 97% of conventional methods for detecting acoustic sources. In the study, intelligent machine learning methods were shown to be effective in detecting Parkinson's disease early.

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