Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets,Aggregation Operators and Basic Uncertainty Information Granule

Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Functional Confirmation Using a Medical X-Ray System of a Semiconductor Survey Meter

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.

Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.

Medical Diagnosis Using Machine Learning:A Statistical Review

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate diagnosis.Machine learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis.Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published.Hence,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this study.To carry out the review,six different databases are selected.Inclusion and exclusion criteria are employed to limit the research.Further,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and results.Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis.The ndings of this study show the most used ML methods and the most common diseases that are focused on by researchers.It also shows the increase in use of machine learning for disease diagnosis over the years.These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.

A consistency contribution based bayesian network model for medical diagnosis

This paper presents an effective Bayesian network model for medical diagnosis. The proposed approach consists of two stages. In the first stage, a novel feature selection algorithm with consideration of feature interaction is used to get an undirected network to construct the skeleton of BN as small as possible. In the second stage for greedy search, several methods are integrated together to enhance searching performance by either pruning search space or overcoming the optima of search algorithm. In the experiments, six disease datasets from UCI machine learning database were chosen and six off-the-shelf classification algorithms were used for comparison. The result showed that the proposed approach has better classification accuracy and AUC. The proposed method was also applied in a real world case for hypertension prediction. And it presented good capability of finding high risk factors for hypertension, which is useful for the prevention and treatment of hypertension. Compared with other methods, the proposed method has the better performance.

Medical Diagnosis Expert System for Malaria and Related Diseases for Developing Countries

There is a strong need for cost-effective technologies to manage disease processes and thus reduce morbidity and mortality in the developing countries. Yet bringing intelligent healthcare informatics to bear on the dual problems of reducing healthcare costs and improving quality and outcomes is a challenge even in countries with a reasonably developed technology infrastructure. This paper focused at how appropriate an ap-plication of Medical Diagnosis Expert System (MDES) is to manage diseases in developing countries. MDES is usually designed to enable clinicians to identify diseases and describe methods of treatment to be carried out taking into account the user capability. The MDES described here is implemented using the C Language Integrated Production System (CLIPS). The CLIPS is an expert system, which has a shell composed of four modules: the user interface, the explanation system, the inference engine and the knowledge base editor. In the system, a number of patient cases will be selected as prototypes and stored in a separate database. The knowledge is acquired from literature review, human experts and the internet of the specific domain and is used as a base for analysis, diagnosis and recommendations.

Clinical diagnosis,treatment,and medical identification of specific pulmonary infection in naval pilots:Four case reports

BACKGROUND Specific pulmonary infection could seriously threaten the health of pilots and their companions.The consequences are serious.We investigated the clinical diagnosis,treatment,and medical identification of specific pulmonary infections in naval pilots.CASE SUMMARY We analyzed the medical waiver and clinical data of four pilots with specific pulmonary infections,who had accepted treatment at the Naval Medical Center of Chinese People’s Liberation Army between January 2020 and November 2021,including three cases of tuberculosis and one of cryptococcal pneumonia.All cases underwent a series of comprehensive treatment courses.Three cases successfully obtained medical waiver for flight after being cured,while one was grounded after reaching the maximum flight life after being cured.CONCLUSION Chest computed tomography scanning should be used instead of chest radiography in pilots’physical examination.Most pilots with specific pulmonary infection can be cured and return to flight.

Intuitionistic Neuro-Fuzzy Optimization in the Management of Medical Diagnosis

Diabetes has become a major concern nowadays and its complications are affecting various organs of a diabetic patient. Therefore, a multi-dimensional technique including all parameters is required to detect the cause, its proper diagnostic procedure and its prevention. In this present work, a technique has been introduced that seeks to build an implementation for the intelligence system based on neural networks. Moreover, it has been described that how the proposed technique can be used to determine the membership together with the non-membership functions in the intuitionistic environment. The dataset has been obtained from Pima Indians Diabetes Database (PIDD). In this work, a complete diagnostic procedure of diabetes has been introduced with seven layered structural frameworks of an Intuitionistic Neuro Sugeno Fuzzy System (INSFS). The first layer is the input, in which six factors have been taken as an input variable. Subsequently, a neural network framework has been developed by constructing IFN for all the six input variables, and then this input has been fuzzified by using triangular intuitionistic fuzzy numbers. In this work, we have introduced a novel optimization technique for the parameters involved in the INSFS. Moreover, an inference system has also been framed for the neural network known as INFS. The results have also been given in the form of tables, which describe each concluding factor.

Comparing upper gastrointestinal X-ray and endoscopy for gastric cancer diagnosis in Korea

AIM:To compare the cost and accuracy of upper gastrointestinal(GI)X-ray and upper endoscopy for diagnosis of gastric cancer using data from the 2002-2004 Korean National Cancer Screening Program(NCSP). METHODS:The study population included 1 503 646 participants in the 2002-2004 stomach cancer screening program who underwent upper GI X-ray or endoscopy.The accuracy of screening was defined as the probability of detecting gastric cancer.We calculated the probability by merging data from the NCSP and the Korea Central Cancer Registry.We estimated the direct costs of the medical examination and the tests for up- per GI X-ray,upper endoscopy,and biopsy. RESULTS:The probability of detecting gastric cancervia upper endoscopy was 2.9-fold higher than via upper GI X-ray.The unit costs of screening using upper GI X-ray and upper endoscopy were$32.67 and$34.89, respectively.In 2008,the estimated cost of identifying one case of gastric cancer was$53094.64 using upper GI X-ray and$16 900.43 using upper endoscopy.The cost to detect one case of gastric cancer was the same for upper GI X-ray and upper endoscopy at a cost ratio of 1:3.7. CONCLUSION:Upper endoscopy is slightly more costly to perform,but the cost to detect one case of gastric cancer is lower.

Screening and early diagnosis of osteoporosis through X-ray and ultrasound based techniques

Effective prevention and management of osteoporosis would require suitable methods for population screenings and early diagnosis. Current clinicallyavailable diagnostic methods are mainly based on the use of either X-rays or ultrasound(US). All X-ray based methods provide a measure of bone mineral density(BMD), but it has been demonstrated that other structural aspects of the bone are important in determining fracture risk, such as mechanical features and elastic properties, which cannot be assessed using densitometric techniques. Among the most commonly used techniques, dual X-ray absorptiometry(DXA) is considered the current 'gold standard' for osteoporosis diagnosis and fracture risk prediction. Unfortunately, as other X-ray based techniques, DXA has specific limitations(e.g., use of ionizing radiation, large size of the equipment, high costs, limited availability) that hinder its application for population screenings and primary care diagnosis. This has resulted in an increasing interest in developing reliable pre-screening tools for osteoporosis such as quantitative ultrasound(QUS) scanners, which do not involve ionizing radiation exposure and represent a cheaper solution exploiting portable and widely available devices. Furthermore, the usefulness of QUS techniques in fracture risk prediction has been proven and, with the last developments, they are also becoming a more and more reliable approach for assessing bone quality. However, the US assessment of osteoporosis is currently used only as a pre-screening tool, requiring a subsequent diagnosis confirmation by means of a DXA evaluation. Here we illustrate the state of art in the early diagnosis of this 'silent disease' and show up recent advances for its prevention and improved management through early diagnosis.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Exploratory Application of X-ray Photography in the Diagnosis of Poultry Fatty Liver

[Objectives]This study was conducted to explore the application value of X ray in the diagnosis of poultry fatty liver.[Methods]Serum biochemical tests were performed on laying hens with suspected fatty liver.The X-ray liver images of the left lateral position were observed.And the liver traits were observed by anatomy.[Results]The increase in liver space occupying lesion observed by X-ray photography was consistent with the increase in liver volume observed by anatomy.The biochemical test showed slight liver function abnormalities.[Conclusions]X-ray examination can be used for the auxiliary diagnosis of poultry fatty liver.

X-ray DIAGNOSIS OF EXTRASKELETAL (SOFT TISSUE) CHONDROSARCOMA (A REPORT OF 8 CASES)

Eight cases of surgically and pathologically verified extraskeletal (soft tissue) chondrosarcoma were analyzed with regard to clinical and X-ray features. The cardinal clinical aspects of this series are: presence of a local soft tissue mass; gradual enlargement of the mass accompanied by increasing pain. The X-ray signs were summarized as follows: formation of a soft tissue mass; various forms of calcifications concentrated in the central area of the tumor; in some instances, presence of a saucer-like defect on the cortical surface of neighbouring bone and periosteal proliferation with mound-like new bone on both sides as well as bending deformity of the affected bone. The incidence and sites of predilection, the main X-ray findings, radiological diagnosis and differential diagnosis of the tumor were discussed. The Roentgen features of synovial chondrosarcoma of the knee joint were especially analyzed.

X-ray DIAGNOSIS OF JUXTACORTICAL CHONDROSARCOMA (A REPORT OF 16 CASES)

The clinical and radiologic features of 16 cases of surgically and pathologically proved juxtacortical chondrosarcoma were analyzed. The main radiograp-hic findings include: (1) A saucerlike or scalloped defect confined to the outer surface of cortex dut to tumor erosion; (2) Localized cortical proliferation and sclerosis; (3) Soft tissue mass and various forms of calcification within the tumor; but without a bony or calcified shell around the tumor mass and (4) sunray and/or laminated periosteal reaction. The origin of this tumor, its histopathology and classification were discussed. A preliminary investigation on the radiated periosteal reaction and its pathological basis was performed.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

Role of Plain Abdominal X-Ray in the Differential Diagnosis of Common Acute Abdominal Conditions in Children at Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State, Nigeria

Background: The plain abdominal x-ray is one of the commonly requested investigations in the children emergency room, paediatric surgical ward and neonatal wards. The short interval required to carry out this investigative procedure and obtain results makes it the first imaging modality used to unravel the different causes of acute abdominal conditions in children. The safety of abdominal x-ray in children makes it attractive for use in paediatric surgical practice as part of routine work-up for undifferentiated acute abdominal conditions and also to diagnose specific causes of acute abdomen in children. Setting: Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State. Objectives: Evaluation of the role of plain abdominal x-ray in diagnosing common acute abdominal conditions in children. Materials and method: Patients admitted to the children emergency room, paediatric surgical wards, children’s ward and neonatal ward who had plain abdominal x-ray as part of their diagnostic work-up were included in the study. They were studied prospectively between March 2011 and April 2021. Results: Three Hundred and Ninety-nine patients who had plain abdominal x-rays as part of their diagnostic work-up were studied. Males were 240 while females were 159, a male to female ratio of 1.5:1. The patients were aged between 1 day to 16 years. Differential diagnoses made with plain abdominal x-ray were intestinal obstruction in 298, perforated viscus 69 patients, intra-abdominal masses 13 patients and location of intra-abdominal foreign body 14. Intestinal obstruction cases in which plain abdominal x-ray played a role in their diagnosis and management included the following: intussusception 66, neonatal sepsis 60, malrotation 48, intestinal atresia 42, anorectal malformation 32, hirschsprung’s disease in 30 cases, pyloric stenosis 24, obstructed hernia 22, post-operative adhesions 16 and intestinal helminthiasis 12. Perforated viscus accounted for 69 indications. Out of these indications, perforated gut in intussusception 19, perforated typhoid ileitis was responsible in 13 cases, gut perforation in blunt abdominal trauma 8, perforation in strangulated hernia 11 cases, perforated gut in malrotation 7, ceacal perforation in hirschsprugs disease 6 and colonic perforation in necrotizing enterocolitis 5 cases. Conclusion: Plain abdominal x-ray remains a role to play in the differential diagnosis and management of common paediatric acute abdominal conditions.

Medical expenditure for esophageal cancer in China:a 10-year multicenter retrospective survey(2002-2011)

Background: Esophageal cancer is associated with substantial disease burden in China, and data on the economic burden are fundamental for setting priorities in cancer interventions. The medical expenditure for the diagnosis and treatment of esophageal cancer in China has not been fully quantified. This study aimed to examine the medical expenditure of Chinese patients with esophageal cancer and the associated trends.Methods: From 2012 to 2014, a hospital-based multicenter retrospective survey was conducted in 37 hospitals in 13 provinces/municipalities across China as a part of the Cancer Screening Program of Urban China. For each esophageal cancer patient diagnosed between 2002 and 2011, clinical information and expense data were extracted by using structured questionnaires. All expense data were reported in Chinese Yuan(CNY; 1 CNY = 0.155 USD) based on the2011 value and inflated using the year-specific health care consumer price index for China.Results: A total of 14,967 esophageal cancer patients were included in the analysis. It was estimated that the overall average expenditure per patient was 38,666 CNY, and an average annual increase of 6.27% was observed from 2002(25,111 CNY) to 2011(46,124 CNY). The average expenditures were 34,460 CNY for stage Ⅰ,39,302 CNY for stage Ⅱ,40,353 CNY for stage Ⅲ, and 37,432 CNY for stage IV diseases(P < 0.01). The expenditure also differed by the therapy type, which was 38,492 CNY for surgery, 27,933 CNY for radiotherapy, and 27,805 CNY for chemotherapy(P < 0.05).Drugs contributed to 45.02% of the overall expenditure.Conclusions: These conservative estimates suggested that medical expenditures for esophageal cancer in China substantially increased in the last 10 years, treatment for early-stage esophageal cancer costs less than that for advanced cases, and spending on drugs continued to account for a considerable proportion of the overall expenditure.

Diagnostic value and safety of medical thoracoscopy for pleural effusion of different causes

BACKGROUND Pleural effusions occur for various reasons,and their diagnosis remains challenging despite the availability of different diagnostic modalities.Medical thoracoscopy(MT)can be used for both diagnostic and therapeutic purposes,especially in patients with undiagnosed pleural effusion.AIM To assess the diagnostic efficacy and safety of MT in patients with pleural effusion of different causes.METHODS Between January 1,2012 and April 30,2021,patients with pleural effusion underwent MT in the Department of Respiratory Medicine,The Second Affiliated Hospital of Xi’an Jiaotong University(Shaanxi,China).According to the discharge diagnosis,patients were divided into malignant pleural effusion(MPE),tuberculous pleural effusion(TBPE),and inflammatory pleural effusion(IPE)groups.General information,and tuberculosis-and effusion-related indices of the three groups were analyzed.The diagnostic yield,diagnostic accuracy,performance under thoracoscopy,and complications of patients were compared among the three groups.Then,the significant predictive factors for diagnosis between the MPE and TBPE groups were analyzed.RESULTS Of the 106 patients enrolled in this 10-year study,67 were male and 39 female,with mean age of 57.1±14.184 years.Among the 74 thoracoscopy-confirmed patients,41(38.7%)had MPE,21 had(19.8%)TBPE,and 32(30.2%)were undiagnosed.Overall diagnostic yield of MT was 69.8%(MPE:75.9%,TBPE:48.8%,and IPE:75.0%,with diagnostic accuracies of 100%,87.5%,and 75.0%,respectively).Under thoracoscopy,single or multiple pleural nodules were observed in 81.1%and pleural adhesions in 34.0%with pleural effusions.The most common complication was chest pain(41.5%),followed by chest tightness(11.3%)and fever(10.4%).Multivariate logistic regression analyses showed effusion appearance[odds ratio(OR):0.001,95%CI:0.000-0.204;P=0.010]and carcinoembryonic antigen(OR:0.243,95%CI:0.081-0.728;P=0.011)as significant for differentiating MPE and TBPE,with area under the receiver operating characteristic curve of 0.977(95%CI:0.953-1.000;P<0.001).CONCLUSION MT is an effective,safe,and minimally invasive procedure with high diagnostic yield for pleural effusion of different causes.