Abstract
Alzheimer’s disease, a progressive neurological disorder, is one of the most common causes of dementia. This is one of the widely studied disorders to understand the changes in the brain and yet there is no cure. Having knowledge of various factors plays an important role in identifying this disease during its various stages of development. The aim of our work is to provide a system to identify the possibility of Alzheimer’s disease during its early stage of progress. This paper presents the analysis of different features of the case studies, as in demented and non-demented, to derive its relation and decide the category. Later the processed data is trained on machine learning models that can fit the data well. The final model will be able to provide a well-generalized hypothesis to classify a case as either likely to be demented or not.
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Bharathi Malakreddy, A., Sri Lakshmi Priya, D., Madhumitha, V., Tiwari, A. (2024). A Comparative Study for Early Diagnosis of Alzheimer’s Disease Using Machine Learning Techniques. In: Hassanien, A.E., Castillo, O., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. ICICC 2023. Lecture Notes in Networks and Systems, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-99-4071-4_16
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DOI: https://doi.org/10.1007/978-981-99-4071-4_16
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