A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease

Antor, Morshedul Bari; Jamil, Ashfaq; Mamtaz, Maliha; Khan, Mohammad Monirujjaman; Aljahdali, Sultan; Kaur, Manjit; Singh, Parminder; Masud, Mehedi

doi:10.1155/2021/9917919

Cited by 85 publications

(29 citation statements)

References 32 publications

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“…To overcome this problem, in the preprocessing stage, 3D images are converted to 2D ones. Another advantage of converting 3D images to 2D images is that the well‐known convolutional networks such as ResNet‐50 and Inception‐V3 designed for 2D natural images can easily be used for 2D medical images by transfer learning 36 . In this article, 3D images of Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset are sampled and 2D images of the Axila, Sagittal and Coronal sections of the brain saved in .png format are used.…”

Section: Methodsmentioning

confidence: 99%

“…Since ResNet50 is the state‐of‐the‐art model in classification 35 and according to paper, 14 inception‐V3 28 has achieved remarkable result in Alzheimer's disease prediction, the results of these two models are combined in the proposed method. Also, SVM always provides the best accuracy among other classification models 36 . Therefore, in this paper, SVM classifier is used in addition to Softmax to get better results.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

Aghaei

Moghaddam

Malek

2022

Int J Imaging Syst Tech

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Early diagnosis of Alzheimer's is crucial to slow the progression of the disease. In this regard, there are many attempts to detect this disease at the early stages using AI techniques such as deep learning. We have proposed an explainable method to solve the early‐stage detection of Alzheimer's using transfer learning as a well‐known approach when there is not enough data. The employed transfer learning method is a combination of fine‐tuned ResNet‐50 and Inception‐V3 with Soft‐max and SVM classifiers using averaging. Moreover, local interpretable model‐agnostic explanations (LIME) are used to show the explainability of the proposed method. The AUC, accuracy, specificity, and sensitivity on structural MRI data of 100 MCI patients were 0.94, 87%, 92%, and 82%, respectively. Also, the LIME results were subjectively evaluated. The results showed the proposed method outperformed some related works. In addition, LIME technique make model more reliable to identify the parts involved in the patient's brain.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

Aghaei

Moghaddam

Malek

2022

Int J Imaging Syst Tech

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“…It can be observed from the literature that classifications of Alzheimer's disease have been done using various machine learning algorithms and the performance of the models has been analysed using one or two algorithms, but in our model, the model performance is analysed using four algorithms. The proposed model is compared with an existing model (Bari Antor et al, 2021) and shows better performance. This paper aims to develop a framework for classifying the various stages of Alzheimer's disease and to analyse the performance of the model using various evaluation metrics.…”

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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“…Data analysis and decision making are crucial steps, especially in mental illness [ 23 , 24 ]. Classification algorithms such as Logistic Regression, Decision Tree, Random Forest, AdaBoost, Naïve Bayes, k-Nearest Neighbor (k-NN), and Support Vector Machine (SVM) are used in different studies [ 25 , 26 , 27 , 28 ] for the diagnosis of patients with Alzheimer’s, Parkinson’s, and mild cognitive impairment (MCI).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Alonso

Marques

Agarwal

et al. 2022

Sensors

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New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

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A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease

Cited by 85 publications

References 32 publications

Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

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

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

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