ANN Algorithms for Parkinson s, ALS, Huntington, and Healthy Walking Detection

In this study, the utilization of artificial neural networks (ANN) algorithms, in the diagnosis of neurodegenerative diseases were examined. Data obtained from the measurement of walking parameters were evaluated for disease diagnosis using the ANN model among individuals with ALS, Parkinson's, Huntington's, and healthy individuals. Comparative analyses conducted using Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms demonstrate that the Levenberg-Marquardt algorithm provides the most effective diagnosis with a success rate of 99%. This study highlights the potential of artificial neural networks in the early diagnosis of neurodegenerative diseases and lays a foundation for future research. In conclusion, artificial neural networks may play a significant role in the diagnosis of neurodegenerative diseases, but further research and method development in this area are warranted.