Multi-view feature selection and classification for Alzheimer’s Disease diagnosis

Zhang, Mingxing; Yang, Yang; Shen, Fumin; Zhang, Hanwang; Wang, Yuan

doi:10.1007/s11042-015-3173-5

Cited by 27 publications

(15 citation statements)

References 30 publications

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“…The author of Reference [ 33 ] presents a new multi-view knowledge gaining approach for the proper diagnosis of Alzheimer’s Disease (AD) using genetics and neuro imaging datasets. At first, a Multi-Layer Multi-View Classification (ML-MVC) method is built to establish the interrelationship between attributes and classes.…”

Section: Literature Reviewmentioning

confidence: 99%

EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis

Mishra

Tripathy

Mallick

et al. 2020

Sensors

138

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Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.

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Section: Literature Reviewmentioning

confidence: 99%

EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis

Mishra

Tripathy

Mallick

et al. 2020

Sensors

138

View full text Add to dashboard Cite

show abstract

“…In parcitular, each weak classifier relies on only one feature and the one with highest score is selected on each iteration. Zare et al [42] propose a similar approach, in which color features are derived from 8 different color spaces and texture features are computed from the GLCM and a treestructured wavelet analysis. Different classifiers and feature selection methods are tested and analyzed.…”

Section: Classification Of Pigmented Skin Lesionsmentioning

confidence: 99%

Enriched dermoscopic-structure-based cad system for melanoma diagnosis

López-Labraca

Fernandez-Torres

González-Díaz

et al. 2017

Multimed Tools Appl

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Computer-Aided Diagnosis (CAD) systems for melanoma detection have received a lot of attention during the last decades because of the utmost importance of detecting this type of skin cancer in its early stages. However, despite of the many research efforts devoted to this matter, these systems are not used yet in everyday clinical practice. Very likely, this is due to two main reasons: 1) the accuracy of the systems is not high enough; and 2) they simply provide a parallel diagnosis that actually does not help to the doctors (as long as there is no way to interpret it).In this paper, we propose a novel approach that aims to provide the doctor with an enriched diagnosis. Specifically, we rely on a dermoscopic-structurebased soft segmentation to design a set of structure-specific classifiers. Each individual structure-specific classifier is trained to distinguish benign lesions from melanomas just paying attention to one type of dermoscopic structure. Then, the outputs of the individual classifiers are combined by a means of the Bayesian method that, besides the final diagnosis, provide the doctor with additional valuable information, such as the opinions of the individual structurespecific experts and the uncertainty of the diagnosis.The results in terms of the features selected for the structure-specific classifiers are consistent with the expert insights. Furthermore, regarding the auto-

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“…Often, these features can be naturally partitioned into groups. Think, for example, about learning with social media data, where one could have features related to the user profile as well as features describing the friend links [1], or when predicting an Alzheimer's disease diagnosis both neuroimaging and genetics data could be used [2], and so on. These groups of features can be referred to as views, and multi-view learning techniques deal with data that is represented by multiple views.…”

Section: Introductionmentioning

confidence: 99%