Hybridization of Machine Learning Models for Alzheimers Disease Classification

Alzheimer's disease (AD), is a gradual cognitive decline and memory impairment. It is a major health concern worldwide. Despite intensive research efforts, accurate and early diagnosis remains difficult to achieve, largely due to the complexity of AD pathology and the absence of definitive biomarkers. Existing diagnostic approaches often rely on costly and invasive procedures, leading to delays in diagnosis and treatment initiation, and limiting the effectiveness of therapeutic interventions. To overcome these issues, this work suggests a novel approach for AD classification using EEG signals. EEG signals offer a non-invasive and cost-effective means of assessing brain activity, making them an attractive candidate for biomarker discovery and disease classification. The proposed work integrates preprocessing, feature extraction, and classification methodologies to accurately differentiate between AD, normal/healthy states, and Frontotemporal Dementia (FTD). The proposed solution begins with Sequential Savitzky-Golay filtering (SEQ-SG) to enhance the quality of EEG signals by reducing noise and enhancing relevant features. Subsequently, an Improved Principal Component Analysis (IPCA) approach is employed for feature extraction, incorporating feature scaling using StandardScaler to ensure uniform contribution from all features. Finally, classification is achieved using a hybrid approach named HMLCAD (Hybridization of Machine Learning for Classification of Alzheimer's Disease), which combines Random Forest and Gradient Boosting through a voting classifier ensemble. This methodology offers a promising framework for accurate and early detection of AD, enabling timely intervention and improved patient outcomes.