Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

Elveren, Erhan; Yumuşak, Nejat

doi:10.1007/s10916-009-9369-3

Cited by 81 publications

(30 citation statements)

References 6 publications

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“…The authors compared their results with previous studies of [62] and [74], who have reported accuracy of 92.30% and 77% respectively. The results of [85] were better than [75], a study on BPwM with two hidden layers classification, which reported accuracy of 93.93%. Reference [85] could not compare their result with [75] on Levenberg-Marquardt (LM) algorithm, because of the complexity of the algorithm.…”

Section: Genetic Algorithmmentioning

confidence: 79%

“…The results of [85] were better than [75], a study on BPwM with two hidden layers classification, which reported accuracy of 93.93%. Reference [85] could not compare their result with [75] on Levenberg-Marquardt (LM) algorithm, because of the complexity of the algorithm. The authors concluded that their classification accuracy for diagnosing TB with GA's is better than previous studies.…”

Section: Genetic Algorithmmentioning

confidence: 79%

“…Concepts of GAs are easy to understand and implement. However, MLNNs with GAs is computationally expensive, as seen in [85]. FL is helpful in the differential diagnosis of the disease and also when a disease has intermediated stages.…”

Section: Resultsmentioning

confidence: 99%

“…Elveren et al, [85] used a Multilayer neural network with two hidden layers and a Genetic Algorithm (GA) for training the algorithm. Reference [85], created a data set of 150 patient's samples, which was divided into two classes, one class with 50 patients who had TB, another class with 100 patients who had non-TB diseases. The feature selection for the input layer and estimation of the performance of the neural network is similar to [75].…”

Section: Genetic Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Meraj¹,

Yaakob²,

Azman³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Tuberculosis (TB) is among top ten causes of deaths worldwide. It is the single most cause of deaths by an infectious disease and is ranked 2nd only after the HIV/AIDS. In third world countries, the diagnosis of TB is done through conventional methods. To diagnostic results are obtain from conventional methods such as blood, culture, sputum and biopsies. They are tedious as well as take long time like 1-2 weeks or maybe evenmore. Therefore, to lower the detection time and raise the accuracy of diagnosis several researches have been carried out. In the past fifty years, due to the advanced and sophisticated technologies, in medical as well as computer science fields, have paved a way to utilize the essence of both the areas. In Artificial Intelligence (AI) various Machine Learning (ML) algorithms have furthered the interests in Computer-aided Detection (CADe) and Diagnosis (CADx) methods. These methodologies assist in medical field for diagnosing the diseases through clinical signs and symptoms as well as radiological images of the patient. They have been implemented for the diagnosis of TB. Advances in AI algorithms, has unveiled great promises in identifying the presence and absence of TB. As of late, many attempts have been made to formulate the strategies to increase the classification accuracy of TB diagnosis using the AI and machine learning approach. This review paper, aims to describes the diverse AI approaches employed in the diagnosis of TB.

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Section: Genetic Algorithmmentioning

confidence: 79%

Section: Genetic Algorithmmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

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Artificial Intelligence in Diagnosing Tuberculosis: A Review

Meraj¹,

Yaakob²,

Azman³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…On the one hand, artificial neural networks have been successfully used in understanding the heterogeneous manifestations of asthma [7], diagnosing tuberculosis [8], classifying leukaemia [9], detecting heart conditions in ECG data [10], etc. These studies show that neural networks have been proven to be capable of dealing with complicated medical data such as the ambiguous nature of the ECG signal data, where neural networks show some outstanding results compared to other methods.…”

Section: Introductionmentioning

confidence: 99%

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

Stamate

Alghamdi

Ståhl

et al. 2018

Communications in Computer and Information Science

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Abstract.In recent years, a number of researches started to investigate the existence of links between cannabis use and psychotic disorder. More recently, artificial neural networks and in particular deep learning have set a revolutionary wave in pattern recognition and machine learning. This study proposes a novel machine learning approach based on neural network and deep learning algorithms, to developing highly accurate predictive models for the onset of first-episode psychosis. Our approach is based also on a novel methodology of optimising and post-processing the predictive models in a computationally intensive framework. A study of the trade-off between the volume of the data and the extent of uncertainty due to missing values, both of which influencing the predictive performance, enhanced this approach. Furthermore, we extended our approach by proposing and encapsulating a novel post-processing k-fold cross-testing method in order to further optimise, and test these models. The results show that the average accuracy in predicting first-episode psychosis achieved by our models in intensive Monte Carlo simulation, is about 89%.

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Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Yilmaz

2024

Concurrency and Computation

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SummaryThe promising results of ML (machine learning) methods in various disciplines have led to the frequent use of these methods in health fields such as disease diagnosis, personalized medicine, medical image‐based diagnosis, and predicting the number of deaths and cases in a pandemic. However, a neglected area in the field of healthcare is the lack of study with ML to predict treatment outcomes for tuberculosis (TB) patients, particularly children experiencing failed treatment. This need has become more apparent as the coronavirus pandemic has reversed the gains of health institutions with TB disease, especially in children. Therefore, this article conducted a study using the stacked ensemble ML method to early predict the risk for children experiencing a failed treatment outcome of TB. To fulfill this need and determine the most appropriate technique, a two‐stage methodology was followed in this work. First, predictions were obtained by combining the information gain feature selection (IGFS) approach with a variety of single‐based ML algorithms, including logistic regression (LR), deep belief neural networks (DBN), random forest (RF), and decision tree (DT). Second, the proposed method, which includes a stacked ensemble ML technique, was used. The latter model uses LR as a meta‐learner and the aforementioned single‐based ML algorithms (DBN, LR, RF, and DT). The performance results of ML models used in the two stages were compared, and the proposed model which is the combination of the stack‐based ensemble learning model and the IGFS technique provided better ROC curves, accuracy, precision, and recall results.

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Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

Cited by 81 publications

References 6 publications

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

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