• SK.Asma Kainath
• Mr.N.Keshava Rao

Dementia poses a growing global health challenge, necessitating innovative approaches for early diagnosis and intervention. This study explores the application of machine learning techniques in the early detection of dementia. Leveraging an extensive dataset comprising clinical, neuroimaging, and genetic information, our research employs state-of-the-art machine learning algorithms to develop a predictive model for identifying early signs of dementia. The study focuses on the integration of diverse data sources, including cognitive assessments, biomarkers, and neuroimaging scans, to enhance the accuracy and reliability of the predictive model. Through rigorous experimentation and cross-validation, our findings demonstrate the efficacy of machine learning in identifying subtle patterns indicative of preclinical stages of dementia. The developed model exhibits promising sensitivity and specificity, showcasing its potential as a valuable tool for clinicians in the early diagnosis of dementia. Additionally, the study highlights the interpretability of the model's features, shedding light on potential biomarkers and cognitive domains crucial for early detection. This research contributes to the ongoing efforts to improve dementia diagnostics, offering a scalable and efficient approach that can be integrated into routine clinical practice. The implications of early detection are profound, enabling timely interventions that may significantly impact patient outcomes and quality of life.

Machine Learning, Stacking Classifier, Dementia Disease, Random Forest, Decision Tree, Voting Classifier, KNN and SVM.

"The early detection of dementia disease using machine learning apporach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.160-164, June-2024, Available :http://www.jetir.org/papers/JETIRGL06028.pdf

"The early detection of dementia disease using machine learning apporach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp160-164, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06028.pdf