Comparative And Stage-wise Alzheimer’s Disease Prediction Using Hybrid Intelligence

• Author(s):

  Ezhilarasi V | Dr. Syed Masood M

• Keywords:

  Clinical Decision Support, Explainable AI, Alzheimer’s Disease, Neuro Data, Machine Learning, Efficient Net

• Abstract:

  In This Paper, Alzheimer’s Disease (AD) Is A Progressive Neurodegenerative Disorder Characterized By Memory Loss And Cognitive Decline. Accurate And Early Diagnosis Is Critical To Intervene Effectively, Yet Reliable And Accurate Diagnosis Is Difficult Because Of The Intricate Relationship Between The Cognitive Symptoms And The Neurological Changes. The Paper Suggests A Hybrid Intelligence Model For Stage-wise Alzheimer’s Disease Prediction Using Cognitive Assessment, Speech Analysis, And Machine Learning Techniques. The Proposed Model Is Composed Of Three Analysis Modules. The First Module Is The Cognitive Assessment Analysis, Where The Data Collected Through Memory-based Tasks, Number Sequence, Visual Recognition, And Questionnaires Are Analyzed Using Machine Learning Techniques To Estimate The Preliminary Cognitive Risk Levels. The Second Module Is The Speech Analysis, Where The Data Collected Through Speech And Language Analysis Are Processed Using Natural Language Processing Techniques To Evaluate The Verbal Fluency, Recall, And Speech Patterns Related To Cognitive Deterioration. The Third Module Is The Multimodal Fusion Analysis, Where The Predictions From The Cognitive And Speech Analysis Are Fused To Generate The Stage-wise Classification Of Alzheimer’s Disease, Ranging From Normal, Mild Cognitive Impairment, And Advanced Stages. Proposed Model Also Considers The Application Of Explainable Artificial Intelligence Techniques To Generate Interpretable Outputs, Facilitating Better Understanding And Decision-making Among The Medical And Caregiving Communities. The Experimental Results Show That The Proposed Model Provides Improved Accuracy And Consistency Compared To The Single-modality Assessment Techniques.

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Other Details

• Paper id:

  IJSARTV12I4104930

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-07

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