Md Asif Nashiry

Work place: Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, 7408, Bangladesh



Research Interests: Computer Science & Information Technology, Computational Science and Engineering, Computational Learning Theory, Computer Architecture and Organization, Data Structures and Algorithms


Md Asif Nashiry received his PhD degree in reversible computing from the University of Lethbridge, Alberta, Canada. He is currently working as an Associate Professor in the department of Computer Science and Engineering of Jashore University of Science and Technology (JUST), Jashore, Bangladesh. His current research interests include machine learning, data science, and bioinformatics. He is the director of the machine learning and data science lab of JUST.

Author Articles
An Optimized Machine Learning Approach for Predicting Parkinson's Disease

By Mousumy Kundu Md Asif Nashiry Atish Kumar Dipongkor Shauli Sarmin Sumi Md. Alam Hossain

DOI:, Pub. Date: 8 Aug. 2021

Parkinson's disease (PD) is an age-related neurodegenerative disorder affecting millions of elderly people world-wide. The early and accurate diagnosis of PD with available treatment might delay neurodegeneration and prevent disabilities. The existing diagnosis method such as brain scan is an expensive process. The use of speech recognition with machine learning technologies for the diagnosis of PD patients could be less expensive. In this work, we have worked with the voice recorded dataset from UCI machine learning repository. Several studies were performed to identify PD patients from the healthy individuals by using voice recorded data with machine learning algorithms. In this paper, we have proposed an optimized approach of data pre-processing that enhances prediction accuracy for diagnosing PD. We obtain 97.4% prediction accuracy with higher sensitivity, specificity, precision, F1 score and kappa value by using AdaBoost. These improved performance evaluation metrics indicate, the use of voice recording with our optimised machine learning approach is highly reliable in prediction of PD. This approach may have significant implications for early stage diagnosis of PD in a cost-effective manner.

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