A Comprehensive Review on Medical Diagnosis Using Machine Learning

Bhavsar, Kaustubh Arun; Abugabah, Ahed; Singla, Jimmy; AlZubi, Ahmad Ali; Bashir, Ali Kashif; Nikita,

doi:10.32604/cmc.2021.014943

Cited by 33 publications

(16 citation statements)

References 61 publications

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“…Recently, machine learning based methods have found widespread use in healthcare, revolutionizing the area of medical diagnosis [ 25 , 26 , 27 ]. To this end, previous attempts for the automatic diagnosis of neuropathies and CTS through machine learning NCS signal processing have shown promising results [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

et al. 2021

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Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.

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Section: Introductionmentioning

confidence: 99%

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

et al. 2021

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“…The emergence of machine learning (ML) in the medical field has revealed the insight of new techniques for hypertension prediction. ML techniques could be used as an early prediction for hypertension disease and could serve as a supporting tool or second opinion in assisting medical doctors in making timely decisions [ 15 ]. Artificial neural network (ANN) models have shown to be a powerful ML technique and exhibited great success in disease prediction and classification [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

Chai

Cheah

Goh³

et al. 2021

Computational and Mathematical Methods in Medicine

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This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F -score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes’ Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes’ Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.

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“…Stellar mass black hole optimization was used for utility mining in [17]. There were also numerous recent published papers dealing with different applications of the machine learning and deep learning approaches, ranging from the sentiment analysis [18], flood prediction [19], the Dengue disease prediction [20], other medical diagnostics [21][22][23], all the way to eHealth and IoT [24][25][26].…”

Section: Background and Related Workmentioning

confidence: 99%

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Bačanin¹,

Alhazmi²,

Živković³

et al. 2022

Computers, Materials &Amp; Continua

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In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing gradient issue. As an alternative approach, stochastic optimization algorithms, such as nature-inspired metaheuristics are more reliable for complex optimization tax, such as finding the proper values of weights and biases for neural network training. In this work, we propose an enhanced brain storm optimization-based algorithm for training neural networks. In the simulations, ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm. The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed, due to the capability of balancing the intensification and diversification and avoiding the local minima. The proposed approach obtained the best accuracy on eight out of ten observed dataset, outperforming all other algorithms by 1-2% on average. When mean accuracy is observed, the proposed algorithm dominated on nine out of ten datasets.

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A Comprehensive Review on Medical Diagnosis Using Machine Learning

Cited by 33 publications

References 61 publications

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

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