• Balwant Kumar
• Sandip Kumar Singh Modak

Alzheimer’s Disease (AD) is a degenerative disorder of the nervous system that slowly impairs memory, thinking ability, and routine activities. Conventional approaches to diagnosing AD mainly depend on symptoms, cognitive tests, and brain imaging, which often detect the disease only after considerable damage has already occurred. In recent years, the rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) has opened new possibilities for identifying AD at much earlier stages. Advanced deep-learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown strong capability in recognising even slight changes and patterns that are often overlooked in MRI and PET scans, speech recordings, as well as genetic information [2], [3], [4]. This chapter provides a detailed discussion of how AI- and ML-based methods contribute to early diagnosis, automated classification, and prediction of disease progression in Alzheimer’s Disease.

Alzheimer’s Disease, biomarkers, cognitive testing, CNN models, deep learning, diagnosis, early detection, EEG signals, machine learning, medical imaging, mild cognitive impairment (MCI), multimodal data, neurodegeneration, neuroimaging, prediction models, progression analysis, RNN models, speech indicators, SVM classifiers, wearable sensors.

"Artificial Intelligence and Machine Learning for Early Detection, Diagnosis, and Progression Prediction of Alzheimer’s Diseases", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.d339-d343, September-2026, Available :http://www.jetir.org/papers/JETIR2609335.pdf

"Artificial Intelligence and Machine Learning for Early Detection, Diagnosis, and Progression Prediction of Alzheimer’s Diseases", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppd339-d343, September-2026, Available at : http://www.jetir.org/papers/JETIR2609335.pdf