Comparative Analysis of Machine Learning and Deep Learning Models of Binary Classification of Alzheimer Disease Using Longitudinal Non-imaging Data

Abstract

The brain is a mass of nerve tissue that controls all of our bodily and cognitive functions. It is susceptible to neurodegenerative diseases such as Alzheimer’s disease (AD). AD is a progressive and irreversible disease that leads to memory loss, continuous cognitive decline, and behavioral deterioration. Early identification of AD stages is crucial for timely intervention and improved patient care. This research compares the performance of multiple Artificial Intelligence (AI) algorithms trained with a longitudinal non-imaging dataset to classify individuals as either ‘Nondemented’ or ‘Demented’. The Support Vector Model (SVM), which is termed long_SVC, achieved the highest performance with an accuracy of 0.829 and an Area under Curve (AUC) of 0.830. The Random Forest model (long_RFC) was the second best, achieving an accuracy of 0.808 and an AUC score of 0.806. Deep learning (DL) approaches, including the long_1D-CNN model and long_LSTM-AE model, performed poorly in differentiating between ‘Non-demented’ and ‘Demented’. The long_1D-CNN model performed better than the long_LSTM-AE model; it had an accuracy of 0.566 and an AUC score of 0.585. The long_LSTM-AE has an accuracy of 0.533 and an AUC score of 0.558. The long_1D-CNN and long_LSTM-AE models had a log loss of 0.683 and 0.708, respectivel. This demonstrated the limited effectiveness of DL algorithms on small datasets. The outcomes showed that traditional machine learning approaches can outperform DL methods when working with limited longitudinal data. This research highlights the potential of the long_SVC model as a decision support tool in healthcare, helping doctors to come to an early diagnosis. Challenges related to real-world deployment, such as model interpretability for doctors, are discussed. Future studies could explore integrating this model into real-world healthcare settings to improve patient outcomes.