Comparative Analysis of the Fuzzy C-Means and Neuro-Fuzzy Systems for Detecting Retinal Disease

Kumar, T. Srinivasa; Kumutha, D.

doi:10.1007/s00034-019-01212-z

Cited by 10 publications

(1 citation statement)

References 14 publications

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“…The widespread adoption of automatic EEG analysis has been driven by several factors, including the robustness of ML algorithms, technological advances, the increasing availability of data, and the affordability of high-performance computing [ 18 ]. In the literature, various classification methods are commonly employed, including neural networks [ 18 , 19 , 20 , 21 , 22 , 23 ] and Support Vector Machines (SVM) [ 24 , 25 , 26 , 27 , 28 ]. Other methods found to be valid for data classification are K-Nearest Neighbour (KNN) [ 18 , 24 , 29 , 30 , 31 ], Gaussian Naïve Bayes (GNB) [ 18 , 32 , 33 ], Adaptive boosting (Adaboost) [ 18 , 24 , 34 , 35 ] and Decision Tree (DT) [ 18 , 24 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Method for Classifying Schizophrenia Patients Based on Machine Learning

Soria

Arroyo

Torres

et al. 2023

JCM

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Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.

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