Big Data Analytics

Medical difficulties like Alzheimer's Disease require improved biomarker finding research. Alzheimer's requires complex methods to find pre-symptomatic markers and improve early diagnosis. Machine Learning and Deep Learning methods like Recurrent Neural Networks (RNN) may help solve these problems. Due to data complexity and training efficiency, optimizing RNN models for Alzheimer's biomarker analysis remains difficult. This research optimizes training and model performance using Stochastic Gradient Descent (SGD) to address these issues. Clinical, genetic, neuroimaging, and digital biomarker data are integrated using Big Data Analytics methods, particularly Multi-Modal Data Fusion. This fusion technique improves accuracy and prediction by examining Alzheimer's biomarkers holistically. This study shows considerable Alzheimer's biomarker discovery advances. The ML, DL, RNN, SGD, and Multi-Modal Data Fusion technique improves early diagnosis models and risk assessment tools. This research sheds light on using sophisticated technologies to better understand and treat Alzheimer's.