Abstract
Alzheimer’s disease (AD) is a kind of neurodegenerative illness that affects memory-related brain cells and capacities. It is one of the most common neurodegenerative disorders that is not unusual. Clinical trials of Alzheimer’s disease pills failed 99.6% of the time. This research looked into machine learning approaches for using empirical statistics to predict Alzheimer’s disease development in the coming years. Diagnosing due to the degree of moderate cognitive impairment, Alzheimer’s disease can be difficult to diagnose, especially early in the illness’s course (MCI). The study purpose of CNN is to discover the most intricate pathways that are directly linked to alterations in Alzheimer’s disease. A range of imaging modalities are employed to diagnose Alzheimer’s disease, and the diagnosis is aided by the use of image modes. Early detection of Alzheimer’s illness, this research employs a CNN for training and a Random Forest Classifier, KNeighborsClassifier, XGBClassifier, and Logistic Regression for testing and classification algorithms. This study looks at how different types of machine learning algorithms can be used to solve AD diagnostic challenges. Deep learning has been proved to be a capable technique for handling a variety of picture identification issues Although the bulk of these published systems owe their effectiveness to rigorous training on a large number of data samples, according to current research.
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Sentamilselvan, K., Swetha, J., Sujitha, M., Vigasini, R. (2022). Alzheimer’s Disease Detection Using Machine Learning and Deep Learning Algorithms. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_29
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