Decision support system for anemia diagnosis based on fuzzy model

A decision support system for the diagnosis of melanoma: A comparative approach

Adjusting the membership functions to satisfy one pattern may deteriorate the inference outcomes of the others. This incompatible issue can be retarded by the extension theory. A novel extension-based fuzzy modeling method, which differs from the traditional fuzzy inference, is proposed. Instead of directly applying the given data to building the fuzzy model, the given data are transformed to another domain by a sigmoidal function to obtain a better fuzzy model. We also define the extended correlation functions to relate the data with the fuzzy sets. During the refining process, the extended fuzzy model, which considers the positive and negative sets simultaneously, is adjusted by the gradient descent method. Simulation results from both single-input-single-output and double-input-single-output systems verified that better results than the conventional methods can be obtained.

Ground vibration is an integral part of the rock blasting process in surface mines, which may cause severe damages to structures and plants in the nearby environment. Therefore, its prediction plays an important role in the minimization of environmental impacts. The peak particle velocity (PPV) is an important predictor for ground vibration. In this paper, first a fuzzy logic model was developed to predict PPV based on collected data from blasting events in Sarcheshmeh copper mine, located in the southwest of Iran. The predictive fuzzy model was implemented on the fuzzy logic toolbox of MATLAB using the Mamdani algorithm. Then, the PPV was predicted by conventional empirical predictors used in blasting practice and also by multiple regression analysis. Finally, a comparative analysis between the results obtained by the fuzzy model and common vibration predictors was carried out. The results indicated the high predictive capacity of fuzzy model, which can be used as a reliable predictor of ground vibration for the studied mine.

A new approach is presented to enhance fuzzy modeling using multidimensional fuzzy sets directly in the fuzzy model in place of decomposed fuzzy sets by projection. The antecedent fuzzy sets in the form of multivariable functions are represented in continuous form. This is accomplished by using a multi input and multi output neural network, which is in particular radial basis functions (RBF) network providing the required multivariable function approximation properties in a fuzzy model. The inputs of the network are fuzzy model inputs, outputs are the membership function values as to the multivariable fuzzy sets involved in the model. Thus, each output is restricted to one multivariable fuzzy set. The RBF network is trained according to the multidimensional fuzzy sets which are identified after fuzzy clustering of the data. Based on this, the linear model parameters are determined by the method of least squares in a straightforward manner. The transparency issues can be handled by means of projection process to have information about the shape and locations of the membership functions pertinent to each variable at the input. The accurate fuzzy modeling having been thus guaranteed the information on the linear model parameters are used to establish the multivariable fuzzy rules with their respective validity regions. The redundancy of the number of fuzzy sets can be circumvented by classical cluster merging algorithms available. In this approach, the fuzzy rules are determined from the local linear models, however in contrast with the conventional fuzzy modeling, the model outputs are determined by the multivariable fuzzy sets. To obtain regular sets, RBF network is trained by means of orthogonal least squares (OLS) method so that at the same time the convergence issues of general neural network training is eliminated.

A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases

The article is devoted to computer modeling of the social and medical problem of determining the degree of obesity of body mass. An analysis was conducted, the significance of the solution to the problem was revealed. Using modern mathematical methods based on the theory of fuzzy sets and the method of fuzzy logical deduction, an approach is presented to determining the degree of obesity of body weight. Based on a short review, it was concluded that the body mass index, waist circumference size, degrees of abdominal obesity are recognized as direct factors in measuring the degree of obesity of body weight. Since all three factors are fuzzy parameters, the method of fuzzy logic inference is used to make a decision. The revealed universes of fuzzy variables of obesity of body weight, body mass index, waist circumference size, degrees of abdominal obesity. With the help of vague production rules, the answer options are provided for various situations. Fuzzy logical inference is implemented by the Mamdani method. The constructed model allows to calculate obesity rates for a large number of people in a short period of time objectively and accurately. Due to this feature, the developed approach can be used in population studies when defining specific categories of body weight as a health problem. The accumulated data of this kind are significant in life insurance. The results are related not only to adverse health problems, but also to social problems, such as determining the fatness of the population of different regions.

Multiple Sclerosis (MS) is one of the most common neurological diseases that it is often progressive and disabling. Its main cause is destruction of myelin sheaths by the immune system. Myelin damage seriously affects people’s physical activities, such as postural impairments. Early detection of the disease is very important in disease management. Unfortunately, currently there is no definite test for MS diagnosis. Of course, there are some tests that help to confirm the diagnosis in advanced stages of the disease butnone of them can independently confirm the disease and have some restrictions and errors. It seems that quantitative analysis of movement disorders especially postural disorders can be helpful in diagnosis of MS even in its early stages. In this study, posturalimpairment was studied. First postural disorders were extracted, then obtained signals were processed quantitatively sovariance and proper frequency features were extracted. At the end, using statistical tests it was shown that these features were significantly different. Therefore based on the results it is possible to design a classifier that can be a firm basis for presenting Decision Support System (DSS) for multiple sclerosis diagnosis.

Stroke remains a leading cause of morbidity and mortality worldwide, necessitating rapid and accurate diagnosis to improve patient outcomes. The gold standard in stroke diagnosis relies on a costly and time-consuming combination of clinical evaluation and neuroimaging techniques, while also being associated with misdiagnoses due to mimicking conditions. This highlights the need for improving the current healthcare system towards faster, more accurate, and low-resource diagnostics. In this direction, this study presents the development of a novel artificial intelligence-driven decision support system for stroke diagnosis using machine learning techniques to fuse standard EHR variables (electronic health records) with information derived from extracellular vesicles (EVs) in blood plasma, which hold promise as diagnostic markers due to their association with stroke pathophysiology. As such, flow cytometry was employed to measure blood samples from 140 patients suffering from stroke or stroke-mimicking conditions. The derived EV-features, including statistical characteristics of their diameters’ distribution and concentration, were integrated with demographical, clinical, and biochemical patient data to form a comprehensive feature set. By utilizing a decision tree learning model, the proposed system achieved a classification weighted accuracy of 86%. This achievement indicates the approach’s potential for integrating cutting-edge photonics-based signals with standard HER information to improve clinical decision-making in emergency settings and for aiding treatment strategies in stroke care.

The implementation of the medical decision support system (MDSS) in clinical practice requires careful monitoring to ensure patient safety and track the performance of artificial intelligence technologies.Goal: To assess the effectiveness of the TOP-3 MDSS in outpatient clinics of the Moscow Heath Care Department.Materials and methods: The Moscow Heath Care Department monitored the TOP-3 operation between October 1, 2020 and March 03, 2024 (n = 63,809,360 people). The Hit-3 metric was used to determine if the MDSS needs retraining. An additional study involving medical experts included a retrospective analysis of data from 3,000 patients and calculation of the diagnostic agreement rate among the MDSS, doctors and the experts.Results: The monitoring demonstrated mean Hit-3 of 63.5, 64.5 and 67.7 for MDSS ver. 1, 2, and 3, respectively. In cases where the clinicians disagreed with the MDSS (n = 2000), based on the patient complaints, experts agreed with the MDSS in 80.2% of cases, with clinicians in 11.5% of cases, and established a different diagnosis in 8.3% of cases. In cases where the clinicians’ and the MDSS’s conclusions matched (n = 1000), experts approved the diagnosis in 50.4% of cases, selected an alternative diagnosis suggested by the MDSS in 37.9% of cases, and established a different diagnosis for 11.7% of patients. Conclusion: The described monitoring methodology, supplemented by expert review, allowed for a comprehensive assessment of the MDSS to be implemented in the health care system. Based on the results of the TOP-3 effectiveness assessment, it was decided that the analyzed list of data from electronic health records should be expanded, which will be implemented in the upcoming version TOP-3+.

An artificial intelligence-based decision support system for early diagnosis of polycystic ovaries syndrome

Computed tomography imaging, a non-invasive tool, is used around the globe by medical professionals to identify and diagnose lung cancer; a lethal disease with high rates of occurrence and mortality globally. Radiomics extracted from medical images, including computed tomography, in tandem with machine learning frameworks has received considerable focus and research for lung nodule identification.This investigation can help out clinicians to reach radiomics-based better and quicker decision support system for treatments and early diagnosis. However, it is still foggy and unclear which radiomics feature(s) to use for the prediction of pulmonary nodule. Consequently, this work is offered with an endeavor to efficiently apply machine learning techniques and radiomics to classify CT pulmonary nodules. Lung Image Data Consortium (LIDC), containing 1018 CT cancer cases, is put to use. The Wavelet Packet Transform is used in conjunction with geometrical features, gray level run length matrix, gray level co-occurrence method and gray level difference method techniques to extract radiomics. Two techniques, boosted and bagged ensemble classification trees, are employed to choose an apposite set of features. The categorization of nodules as malignant or benign is assessed by the utilization of cutting-edge machine learning models: Support Vector Machines, Boosted Classification Ensemble Tree, Decision Trees,Bagged Classification Ensemble Tree, RUSBoosted Ensemble Trees, Subspace Discriminant Ensemble andSubspace KNN Ensemble. The findings reveal that the Ensemble Subspace KNN gives best AUROC (93.4%), accuracy (88.3%) and F1-score (85.2%) using BACET feature selection method. The best sensitivity is produced by FGSVM (97.1%). RUSBOCET gives best precision and specificity of 93.4% and 83.1% respectively. Lung Cancer remains themost common and deadly type of cancer. Early detection of lung lesionsand nodules is crucial in the fight against lung cancer. The purpose of this study was to investigate radiomics based on geometrical, texture, and Daubechies WPT texture features for quantitative CT image analysis. The LIDC database wasused in this study. Geometrical features, texturefeatures based on three statistical methodologies (GLCM, GLDM GLRLM) and Daubechies WPT texture features are retrieved fromthe nodules. Using the ensemble EFS, BOCETand BACET,pertinent featureswere identified. Lastly, various cutting-edge ML classifiers were used to classify LC as malignant or benign. The out-turn shows that, using BACET EFS, Ensemble Subspace KNN gives best AUROC (93.4%), accuracy (88.3%) and F1-score (85.2%). FGSVM yields the best sensitivity of 97.1%. RUSBOCET gives best precision and best specificity of 93.4% and 83.1% respectively. Therefore, the methodology can be applied with efficacy to the CT based PN classification. Thus, the result can assist medical professionals in making better decisions and interventions.

Liver diseases have been shown to be highly correlated to excessive consumption of alcohol and other harmful or injurious substances such as drugs and toxins.The Nigeria social milieu cannot do away with excessive consumption of alcoholicrelated substances and drugs, which are predominantly consumed on weekends either in parties or clubs.Most Nigeria teenagers and adults alike who are supposedly considered as socially correct indulge in excessive consumption of alcohol and other harmful substances leaving the alcoholic companies and shops smiling to the banks.This unpalatable trend has dire consequences as it raises the figure of liver disease patients, which is mostly confused with other tropical diseases like malaria and as such its manifestation cannot be predicted on time with certainty.Timely diagnosis is a panacea to the management of the disease but this is not the case most times as there are handful of hepatologists that can adequately diagnose this disease since General practitioner might not be able to diagnose them on time.This research seeks to comparatively analyze the performance of Neutrosophic-based Decision Support System and Multilayer Neural Network (Traditional Neural Network) in the classification of Indian Liver Patient Dataset (ILDP) as well as articulate its suitability in the classification or diagnosis of liver disease (ILPD).Object Oriented Analysis and Design methodology was used while the implementation was done using WEKA and Java on a Netbeans platform.Experimental results show that Neutrosophic-Based Decision Support System (NBDSS) with an accuracy of 96.41% using a confusability measurement threshold of 0.003278 performed better than the conventional neural network with an accuracy of 72.45%.This clearly shows that Neutrosophic-based Decision Support System is suitable for the diagnosis of liver diseases.

The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI- class 1), Astrocytoma (AST- class 2), and Normal brain regions (NORM- class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

A Clinical Decision Support System for Early Diagnosis of Behçet’s Disease

Information Technology is playing a game changing role in life of human being. Healthcare is one of the prime concerns of every human being. This research work is based on diabetes, a chronic disease which is very common in all over the world. A decision support system may help doctors for decision-making and it may also support to an individual to take decision after filling the details of his or her diagnosis report. In this research work, development of a decision support system based on ant colony optimized neural network has been done which is hybrid of feature selection with ant colony neural network. A comparative analysis of ant colony optimized neural network and hybrid ant colony optimized neural network on the basis of sum of squared error is also performed in this research work.