Diagnosis of Alzheimer Disease from Multi-Modal Data: Role of Artificial Intelligence

Alzheimer's disease (AD) is another area of medical research concern mainly due to the current prognosis of the disease that is further impacting the patient and health care organizations. This paper aims at investigating the performance of the predictive model that employs artificial intelligence (AI) to forecast Alzheimer's disease based on clinical histories, neuro-images, and genomics. The CNN integrated with LSTM can allow for the analysis of such multifaceted data sources since it has outstanding performance in processing and interpreting the data. To understand Alzheimer's disease, we used a combination of CNNs and LSTMs. CNNs helped us identify patterns in brain scans, while LSTMs analyzed how clinical and genetic factors changed over time. The following model defines an enhanced decision-making approach that could prove useful in early diagnosis and treatment regimen planning compared to standard approaches. Based on the findings, multi-modal data contributes significantly to the identification of Alzheimer's disease prediction with high accuracy and less variability so that fruitful intervention trials and research would be well pioneered strategies.