<b>predALZ: Advanced Computational Ensemble Techniques for Identifying Alzheimer's Biomarkers within Genomic Profiles</b>

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder with a substantial genetic contribution, especially in early-onset case. Mutations in genes like APP, PSEN1, and PSEN2 serve as crucial biomarkers, indicating a heightened risk of developing AD. Our proposed prediction model, grounded in established genetic insights, employs genomic sequence analysis to drive advancements in early Alzheimer's disease (AD) detection and enable the development of targeted therapeutic interventions. predALZ is proposed by integrating genomic data from GWAS and utilized for advanced feature extraction techniques. A comprehensive set of classifiers, encompassing Ensemble methods, XGBoost, Random Forest, LightGBM, and ExtraTrees, employed to train the predALZ model, utilizing the generated feature vector for training.