An Early Prediction and Detection of Alzheimer's Disease: A Comparative Analysis on Various Assistive Technologies

Subetha, T.; Khilar, Rashmita; Sahoo, Sarat Kumar

doi:10.1109/cispsse49931.2020.9212240

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…As the population ages, it is predicted that this number will increase. For illustration, by 2050, it is predicted that those 60 and older would make up 20% of the population in India suffering with this disease [2]. Moreover, there will be 13.8 million Americans by the year 2050 that are affected with a projected prevalence of AD.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Alzheimer's Disease Detection

Goyal

Rani

Singh

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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Alzheimer's disease (AD), a psychiatric problem, availing between those who are 65 and older. Additionally, the disease's steady development of a variety of visible and invisible symptoms, such as irritability and aggression, has a substantial negative impact on a patient's overall quality of life. Although many treatments have been developed to help reduce its symptoms, AD has no known cure. As a result, the field of AD management is growing, and a comprehensive framework for the early detection of AD must be created. In this study, we created three classification models for predicting AD using machine learning and five models for predicting AD using ensemble learning. SVM, DTs, and RF are the three basis classifiers employed in the current work. Five ensemble classifiers, XGBoost, Voting Classifier, Extra Trees (ETs) Classifier, Gradient Boost, and AdaBoost, are then thoroughly compared. After thoroughly inspecting the dataset for outliers or other noise, a feature selection method known as PCA and several preprocessing techniques are used to lessen the issue of overfitting and performance enhancement. Additionally, this study utilised the longitudinal information from the OASIS website, which included 150 patients overall, 72 of whom were not demented and 78 of whom were. The RF model, which had an accuracy of 83.92% compared to the other two base classifiers, provided the best classification performance, while the ETs Classifier, an ensemble classifier, performed the best when compared to base and ensemble classifiers, with an accuracy of 86.60%.

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Section: Introductionmentioning

confidence: 99%