An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD)

Syed, Asif Hassan; Khan, Tabrej; Hassan, Atif; Alromema, Nashwan; Binsawad, Muhammad; Alsayed, Alhuseen Omar

doi:10.1109/access.2020.3043715

Cited by 18 publications

(9 citation statements)

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“…For the delineation of salinity susceptibility maps, [24] performed three machine learning models: Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) which he fed with 16 variables, selecting by RFE 8 which gave better results, obtaining an accuracy of 87%. [25] used a combination of RFE with L1 regularization to predict Alzheimer's disease in early stages through CSF protein biomarkers, subsequently using the SVM classifiers and logistic regression, achieving a precision close to 95.24%. [26] to discover the hidden factors that influence criminal activities in New York City, he performs models through Random Forests, artificial neural networks, SNM, logistic regression, gradient-increasing decision trees; Using previously for the selection of RFE variables, achieving accuracy in the best of cases of 80.9%.…”

Section: Description and Analysis Of The Resultsmentioning

confidence: 99%

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

et al. 2021

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Currently, the generation of alternative energy from solar radiation with photovoltaic systems is growing, its efficiency depends on internal variables such as powers, voltages, currents; as well as external variables such as temperatures, irradiance, and load. To maximize performance, this research focused on the application of regularization techniques in a multiparametric linear regression model to predict the active power levels of a photovoltaic system from 14 variables that model the system under study. These variables affect the prediction to some degree, but some of them do not have so much preponderance in the final forecast, so it is convenient to eliminate them so that the processing cost and time are reduced. For this, we propose a hybrid selection method: first we apply the elimination of Recursive Feature Elimination (RFE) within the selection of subsets and then to the obtained results we apply the following contraction regularization methods: Lasso, Ridge and Bayesian Ridge; then the results were validated demonstrating linearity, normality of the error terms, without autocorrelation and homoscedasticity. All four prediction models had an accuracy greater than 99.97%. Training time was reduced by 71% and 36% for RFE-Ridge and RFE-OLS respectively. The variables eliminated with RFE were Total Energy, Daily Energy, and Irradiance, while the variable eliminated by Lasso was: "Frequency". In all cases we see that the root mean square errors were reduced for RFE. Lasso by 0.15% while for RFE-Bayesian Ridge by 0.06%.

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Section: Description and Analysis Of The Resultsmentioning

confidence: 99%

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

et al. 2021

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“…Feature extraction was also a focus of the paper. Syed et al (2020) proposed an ensemble classification model using the linear SVM and LR algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

Rajayyan¹,

Mustafa²

2023

AIP

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Alzheimer's disease is a brain memory loss disease. Usually, it will affect persons over 60 years of age. The literature has revealed that it is quite difficult to diagnose the disease, so researchers are trying to predict the disease in the early stage. This paper proposes a framework to classify Alzheimer's patients and to predict the best classification algorithm. The Bestfirst and CfssubsetEval methods are used for feature selection. A multi-class classification is done using machine learning algorithms, namely the naïve Bayes algorithm, the logistic algorithm, the SMO/SMV algorithm and the random forest algorithm. The classification accuracy of the algorithms is 67.68%, 84.58%, 87.42%, and 88.90% respectively. The validation applied is 10-fold cross-validation. Then, a confusion matrix is generated and class-wise performance is analysed to find the best algorithm. The ADNI database is used for the implementation process. To compare the performance of the proposed model, the OASIS dataset is applied to the model with the same algorithms and the accuracy of the algorithms is 98%, 99%, 99% and 100% respectively. Also, the time for the model construction is compared for both datasets. The proposed work is compared with existing studies to check the efficiency of the proposed model.

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“…Recent studies have explored how to improve prediction accuracy by fusing deep learning and ensemble learning systems [19]. Ensembling has recently become more significant in separating cognitively healthy people from a progressive form of MCI that eventually results in AD [20][21][22].…”

Section: Literature Reviewmentioning

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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An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD)

Cited by 18 publications

References 50 publications

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

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

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