• M.Mallikarjuna Rao
• V.Sasikala
• Cherukuri Bhumika
• Guduru RishiUdayaSri
• Kodavali Aswani , Madhamanchi Chaithanya

Alzheimer’s disease is an incurable neuro degenerative disease that affects brain memory mainly in aged people. Alzheimer’s disease occurs worldwide and mainly affects people aged older than 65 years. Early diagnosis for accurate detection is needed for this disease. Manual diagnosis by health specialists is error prone and time consuming due to the large number of patients presenting with the disease.Various techniques have been applied to the diagnosis and classification of Alzheimer’s disease but there is a need for more accuracy in early diagnosis solutions. Alzheimer's disease (AD) detection is a critical aspect of managing this progressive disorder, characterized by early cognitive decline and memory loss. Effective detection is crucial for initiating timely interventions that can slow disease progression and improve patient quality of life. This abstract provides an overview of the current methods and advancements in Alzheimer's disease detection.Traditional diagnostic approaches include comprehensive clinical evaluations, cognitive assessments, and techniques such as MRI and PET scans, which help visualize brain changes associated with AD.Additionally, biochemical analyses of fluid (CSF) and blood biomarkers offer insights into the presence of pathological proteins like amyloid-beta and tau.Recent advancements in machine learning and artificial intelligence are enhancing diagnostic accuracy by analyzing complex patterns inand genetic data. Despite these advancements, challenges persist, including the need for more sensitive and specific biomarkers, integration of diverse diagnostic tools, and addressing disparities in access to diagnostic resources. Ongoing research and technological innovations are pivotal in refining detection methods, promoting early diagnosis, and ultimately providing better support and treatment for individuals affected by Alzheimer's disease.

Alzheimer’s disease, Diagnosis, Biomarkers, Biochemical, Clinical, Genetic, Pathological proteins, cognitive assessments, MRI and PET scans, Diagnostic tools

"ALZHEIMER’S DISEASE DETECTION USING RANDOMFORESTCLASSIFIER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.312-321, November-2024, Available :http://www.jetir.org/papers/JETIRGO06031.pdf

"ALZHEIMER’S DISEASE DETECTION USING RANDOMFORESTCLASSIFIER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp312-321, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06031.pdf