A Deep Learning Approach for Prediction of Neurodegenerative Disease Progression

Abstract

Many Neurodegenerative disease like Alzheimer’s disease, Parkinson’s disease, amd amyotrophic lateral sclerosis (ALS) are generally quite difficult to provide solution as they have a very complex nature of progress. Disease progression is quite important to predict to that timely treatment and personalized planning for treatment can be done in time to get the desired outcomes. The traditional treatment methods are sometime not sufficient to handle the large amount of data that is generated from neuroimaging, genomics and clinical records. This paper proposes a framework based on deep learning to predict the neurodegenerative disease progression using multimodal data integration. The proposed method uses the convolutional neural networks (CNNs) for imaging data and recurrent neural networks (RNNs) for longitudinal clinical data. The proposed models give more accurate predictions based on deep learning algorithms and gives a valuable insight into the disease progression. This paper uses the data from open source data sets including the Alzheimer’s disease neuroimaging initiative (ADNI), for evaluating the performance of the algorithms. The results thus obtained show a significant improvement in the accuracy obtained in prediction of the disease progression as compared to the traditional approaches. Presented research gives an insight into the use of artificial intelligence (AI) for improving the early detection, monitoring and controlling of neurodegenerative diseases, and showing the way for more precise machine learning and an improvement in the human lisestyle for the affected patients.

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