New fast normalized neural networks for pattern detection

El-Bakry, Hazem M.; Mastorakis, Nikos E.

doi:10.1016/j.imavis.2007.02.001

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Accurate diagnostic processes are crucial for treatment, in the realm of health given the challenges associated with precise psychiatric diagnoses owing to the overlapping symptoms of various mental illnesses making it difficult to differentiate or diagnose them accurately. This is to get the right psychiatric diagnosis before starting any treatment plan [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Diagnosis of Depression and Anxiety Through Explainable Machine Learning Methods

Marey,

Salem,

Rashidy

et al. 2024

IJACSA

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Diagnosing depression and anxiety involves various methods, including referenda-based approaches that may lack accuracy. However, machine learning has emerged as a promising approach to address these limitations and improve diagnostic accuracy. In this scientific paper, we present a study that utilizes a digital dataset to apply machine learning techniques for diagnosing psychological disorders. The study employs numerical, striatum, and mathematical analytic methodologies to extract dataset features. The Recursive Feature Elimination (RFE) algorithm is used for feature selection, and several classification algorithms, including SVM, decision tree, random forest, logistic regression, and XGBoost, are evaluated to identify the most effective technique for the proposed methodology. The dataset consists of 783 samples from patients with depression and anxiety, which are used to test the proposed strategies. The classification results are evaluated using performance metrics such as accuracy (AC), precision (PR), recall (RE), and F1-score (F1). The objective of this study is to identify the best algorithm based on these metrics, aiming to achieve optimal classification of depression and anxiety disorders. The results obtained will be further enhanced by modifying the dataset and exploring additional machine learning algorithms. This research significantly contributes to the field of mental health diagnosis by leveraging machine learning techniques to enhance the accuracy and effectiveness of diagnosing depression and anxiety disorders.

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Section: Related Workmentioning

confidence: 99%

Enhancing the Diagnosis of Depression and Anxiety Through Explainable Machine Learning Methods

Marey,

Salem,

Rashidy

et al. 2024

IJACSA

View full text Add to dashboard Cite

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“…The initial stage in bagging is to generate several models with different datasets based on the bootstrap sampling technique [45][46][47]. Each set of samples includes random samples from the original data.…”

Section: Proposed Work Proposed Machine Learning Modelmentioning

confidence: 99%

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

et al. 2023

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Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

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“…Data scaling methods in machine learning are employed to address the importance of scalability and ensure accurate outcomes while minimizing uncertainties, incorrect predictions, and additional costs or processing time. One common approach to data scaling involves transforming the minimum value of a feature to 0 and the maximum value to 1 [6,9,[42][43][44][45][46][47][48][49]. In our study, we applied data scaling using the following equation:…”

Section: Data Scalingmentioning

confidence: 99%

Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome

Elseddik,

Alnowaiser,

Mostafa

et al. 2023

Diagnostics

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Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients’ health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.

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New fast normalized neural networks for pattern detection

Cited by 8 publications

References 19 publications

Enhancing the Diagnosis of Depression and Anxiety Through Explainable Machine Learning Methods

Enhancing the Diagnosis of Depression and Anxiety Through Explainable Machine Learning Methods

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome

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