Medical decision support systems based on Fuzzy Cognitive Maps

Habib, Shaista; Akram, Muhammad

doi:10.1142/s1793524519500694

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…Brand marketing requires effective use of data to continuously make various scientific and reasonable decisions. Therefore, research on decision support systems that can assist business managers in making scientific decisions is of great significance [1]. Parallel computing is now mainly used in scientific research and image rendering.…”

Section: Introductionmentioning

confidence: 99%

Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

Liu

2022

Wireless Communications and Mobile Computing

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With the rapid development of information technology, decision support systems that can assist business managers in making scientific decisions have become the focus of research. At present, there are not many related studies, but from the brand marketing level, there are not many studies combining smart technology. Based on computer vision technology and parallel computing algorithms, this paper launches an in-depth study of brand marketing decision support systems. First, use computer vision technology and Viola-Jones face detection framework to detect consumers’ faces, and use the classic convolutional neural network model AlexNet for gender judgment and age prediction to analyze consumer groups. Then, use parallel computing to optimize the genetic algorithm to improve the running speed of the algorithm. Design the brand marketing decision support system based on the above technology and algorithm, analyze the relevant data of the L brand, and divide the functional structure of the system into three parts: customer market analysis, performance evaluation, and demand forecasting. The ROC curve of the Viola-Jones face detection framework shows its superior performance. After 500 iterations of the AlexNet model, the verification set loss of the network is stable at 1.8, and the accuracy of the verification set is stable at 38%. Parallel genetic algorithms run 1.8 times faster than serial genetic algorithms at the lowest and 9 times faster at the highest. The minimum prediction error is 0.17%, and the maximum is 2%, which shows that the system can make accurate predictions based on previous years’ data. Computer vision is a technique that converts still image or video data into a decision or a new representation. All such transformations are done to accomplish a specific purpose. Therefore, a brand marketing decision support system based on computer vision and parallel computing can help managers make scientific decisions, save production costs, reduce inventory pressure, and enhance the brand’s competitive advantage.

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Section: Introductionmentioning

confidence: 99%

Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

Liu

2022

Wireless Communications and Mobile Computing

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“…Computer-aided diagnosis has become a major research subject in many healthcare related applications. CAD automatically makes efficient decisions and facilitates medical experts to make fast decisions [18][19][20][21][22]. In the latest research works [23,24], CXR and CT images have been used for training Deep Learning (DL) models to diagnose COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

Naseer

Tamoor

Azhar

2022

XST

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Background: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19. Methods: In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve. Results: We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images. Conclusions: We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.

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“…This section provides a comparative analysis of the proposed de-neutrosophication formula with the existing de-neutrosophication formula, fuzzy soft-sets, and fuzzy cognitive maps with the help of various data sets [9], [37]- [38]. In the literature, many methods of decision making are discussed.…”

Section: Comparison Analysismentioning

confidence: 99%

A neutrosophic clinical decision-making system for cardiovascular diseases risk analysis

Habib

Salam

Butt

et al. 2020

IFS

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Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirty-five symptoms as input parameters, which are, gender, age, genetic disposition, smoking, blood pressure, cholesterol, diabetes, body mass index, depression, unhealthy diet, metabolic disorder, physical inactivity, pre-eclampsia, rheumatoid arthritis, coffee consumption, pregnancy, rubella, drugs, tobacco, alcohol, heart defect, previous surgery/injury, thyroid, sleep apnea, atrial fibrillation, heart history, infection, homocysteine level, pericardial cysts, marfan syndrome, syphilis, inflammation, clots, cancer, and electrolyte imbalance and finds out the risk of coronary artery disease, cardiomyopathy, congenital heart disease, heart attack, heart arrhythmia, peripheral artery disease, aortic disease, pericardial disease, deep vein thrombosis, heart valve disease, and heart failure. There are five main modules of the system, which are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To demonstrate the complete working of our system, we design an algorithm and calculates its time complexity. We also present a new de-neutrosophication formula, and give comparison of our the results with existing methods.

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Medical decision support systems based on Fuzzy Cognitive Maps

Cited by 13 publications

References 22 publications

Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

A neutrosophic clinical decision-making system for cardiovascular diseases risk analysis

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