What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Machine learning-based early detection of Parkinson’s disease using handwriting and vocal features

Puntos clave

DenseNet-121 achieved an accuracy of 85.17% for handwriting analysis, indicating effectiveness in detecting motor control differences.
Speech analysis via SVM reached an accuracy of 89.74%, highlighting its role in identifying non-motor symptoms of parkinsons disease.
Combining handwriting and speech analysis provides a more comprehensive diagnosis, improving patient intervention compared to traditional methods.
Machine learning classifiers, including SVM and others, have shown significant potential in enhancing parkinsons disease detection methods.

Resumen

This study investigates handwriting and speech patterns in individuals with Parkinson's disease (PD) using machine learning to enable early disease detection—a critical step for effective treatment. Handwriting analysis centers on motor components, such as spiral angle variation and wave amplitude, which reflect the impaired fine motor control characteristic of PD. Among deep learning models evaluated (ResNet, AlexNet, DenseNet, and VGG16), the DenseNet-121 model achieved the highest accuracy of 85.17% for classifying motor control differences. Voice analysis targets non-motor symptoms, focusing on speech disturbances linked to tremors and muscle rigidity. Machine learning classifiers (SVM, KNN, MLP, XGBoost, Logistic Regressor, and Random Tree) were implemented, with SVM demonstrating the best performance by reaching an accuracy of 89.74% alongside strong precision and recall. Combining handwriting and speech analysis offers a more comprehensive and effective PD diagnosis than conventional clinical approaches, facilitating prompt intervention for improved patient care.

Preguntar a la IA

Me gusta

Guardar