Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2346 2018 2018 Director de la Universidad Regional Autónoma de los Andes (UNIANDES) Sede Santo Domingo, Ecuador Fredy Cañizares Galarza Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador Luis Freire Lescano Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador Lina Espinoza Neri Tashkent State University of Economics, Uzbekistan Dilafruz Nabieva 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. 2024 2024 273 282 10.54216/FPA.140120 https://www.americaspg.com/articleinfo/3/show/2346