Paper “Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics” by A. Ćuk, T. Bezdan, L. Jovanović, M. Antonijević, M. S. Stanković, V. Simić, M. Živković & N. Bačanin had been published in journal Scientific Reports.

The paper studies the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson’s disease based on dual-task walking test data. Given that the performance of neural networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) was proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson’s disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.