A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease

Fredy Cañizares Galarza^1,*, Luis Freire Lescano², Lina Espinoza Neri², Dante Manuel M. Fernández³, Dilafruz Nabieva⁴

¹Director de la Universidad Regional Autónoma de los Andes (UNIANDES) Sede Santo Domingo, Ecuador

²Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador

³Universidad Nacional Mayor de San Marcos, Peru

⁴Tashkent State University of Economics, Uzbekistan

Emails: dir.santodomingo@uniandes.edu.ec; ciad@uniandes.edu.ec; ua.linaespinoza@uniandes.edu.ec; dmfedu@gmail.com; della.nab27@gmail.com

Abstract

Diagnosing Parkinson's Disease (PD) can be quite challenging as it presents with symptoms and lacks biomarkers. Nevertheless, the use of data fusion, which combines types of data using machine learning techniques holds promise, for the timely detection of the disease. In this study, we explore the application of data fusion by employing Principal Component Analysis (PCA) as a step to reduce complexity. We then utilize the K Nearest Neighbors (KNN) classification to improve the accuracy of PD diagnosis. By analyzing nonlinear features associated with PD from a dataset PCA helps us extract attributes while maintaining important variations in the data. Subsequently, KNN is employed to identify patterns in this reduced feature space and effectively distinguish between individuals with PD and those who are healthy. Our results show improvements when using the KNN classifier compared to state-of-the-art approaches. This demonstrates its effectiveness in detecting PD leading to promising outcomes and providing a framework for precise PD diagnosis.

Keywords: data fusion; Machine learning; Parkinsonian symptoms; Data-driven diagnosis; Neurological disorder; Pattern recognition techniques; Diagnostic accuracy assessment