The project is aimed at developing and evaluating artificial intelligence and machine learning models for the detection of Parkinson’s disease using a comprehensive dataset. The dataset contains sensor data related to ground reaction forces in both feet during various activities. The project involved the training and evaluation of 3 machine learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a combined CNN and LSTM model (CNN + LSTM). The goal was to determine the most effective model for Parkinson’s disease detection and explore the impact of hyperparameter tuning on model performance. Dive into world of Artificial Intelligence and Machine Learning with us.
More Details at : Nature.com
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