Deep Ensemble Learning For Accurate Prediction Of Neurodegenerative Disorders Using Temporal Clinical Data

Abstract

abstract— as stated the proposed research seeks to work on designing a resilient prediction model on neurodegenerative disorders through deep ensemble learning. alzheimer’s and parkinson’s diseases and other related diseases are disorders that involve the gradual loss of neurons. early identification of disorder is highly essential since proper care and treatment can only be given if disorder is predicted accurately. regarding challenge we suggest the deep ensemble learning approach  incorporating several deep learning models, namely recurrent neural networks (rnns), long short-term memory (lstm) networks to analyze temporal structure of the patient records and clinical data. the lstm model is specifically effective in registering long term temporal dependencies while rnns are especially useful in modelling temporal sequentiality. these models are integrated using ensemble learning with a weighted voting system to boost up the model’s predictive prowess and to minimize the problem of overfitting the data. because of this, the ability of the ensemble to harness strengths from each of the individual models makes the whole system very resilient, even if patient data is noisy or otherwise partially complete. substantial testing and verification on clinical datasets show that the introduced approach outperforms existing ones in identifying the risk factors of neurological disorders and their evolution, which can help improve the accuracy of clinical decision-making and improve patient care.