Prognosis of Liver Disorders in Dna Positive Hbv Patients Based on Fuzzy Soft Sets

Liver disease and disorders are serious public health burdens because of the high prevalence among populations worldwide and poor long-term clinical outcome. The outcomes of the disease include deaths from liver decompensation, cirrhosis and HCC. Many liver diseases, including chronic HBV and HCV infection, ALD, NAFLD, autoimmune liver disease and drug-induced liver disease (DILI), potentially threaten a large proportion of the global population. The fuzzy soft set principle theory has been used for the developing of a diagnostic system in medicine and devise a prediction system named as fuzzy soft expert system which is a rule-based system uses fuzzy set and fuzzy soft set. There are five main components included in the basic structure, they are: (1) A fuzzication that translates the inputs (real-values) into fuzzy values, (2) obtaining fuzzy sets, (3) changing in to fuzzy soft sets, (4) reduction of normal parameter of fuzzy soft sets, (5) output data by algorithm. Fifty two individuals suspected and managed as HBV patients were involved in this study.All of them were attending liver diseases unit at Azadi teaching hospital in Duhok, Kurdistan Region-Iraq. They were being managed by the herpetology specialist as HBV infected patients. Their parameters (Alanine Aminotransferase (ALT), Aspartate aminotransferase (AST), Total Serum Albumin (Alb.), and Total Serum Bilirubin (T.S.Bil.)), were used as input data and the score of each patient was calculated. The developed fuzzy soft expert system was used to obtain the score for each as prognostic model for liver disorders. The score of 10 of those patients are selected and compared with the clinical status of each base on signs and symptoms of the HBV infection. Score more than 101.844 was considered to be highly linked with HBV infection. Scores less than 101.844 was considered to be not related to HBV infection

Medical Data Classification Using Similarity Measure of Fuzzy Soft Set Based Distance Measure

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers

METODE ADABOOST PADA SKEMA PEMODELAN HYBRID UNTUK KLASIFIKASI PENYAKIT LIVER

Penelitian ini mengajukan perbandingan antara dua model hybrid yaitu, Artificial Neural Network (ANN) dan Adaboost pada skema pemodelan hybrid untuk klasifikasi penyakit liver. Digunakan metode Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Rough Set (RS), Artificial Neural Network (ANN), dan Adaboost untuk membangun skema model hybrid. Ada dua tahapan utama dalam penelitian ini, tahapan pertama menggunakan LR, MARS, dan RS untuk memilih fitur yang relevan terhadap klasifikasi dan selanjutnya fitur-fitur terpilih akan digunakan sebagai masukan pada klasifikasi menggunakan classifier ANN dan Adaboost. Tahap kedua adalah membangun skema hybrid yang menghasilkan enam kombinasi yaitu LR-ANN, MARS-ANN, RS-ANN, LR-Adaboost, MARS-Adaboost, dan RS-Adaboost. Penelitian ini juga membandingkan akurasi menggunakan classifier tunggal dengan model skema hybrid untuk klasifikasi penyakit liver. Secara keseluruhan, dengan menggunakan skema hybrid Adaboost, akurasi meningkat terhadap classifier tunggal Adaboost

Towards Designing A Hierarchical Fuzzy System for Early Diagnosis of Heart Disease

Heart disease may represent a range of conditions that affect our heart. Disease under heart diseases umbrella include coronary heart disease, heart attack, congestive heart failure, and congenital heart disease, is the leading cause of death. Mor eover, heart disease not only attacks the elderly. In the present day, lots of younger people might be getting affected by the number of heart diseases. In order to decrease the mortality rate caused by heart disease, it is necessary for the disease, to be diagnosed at an early stage. In this paper, we have proposed the use of hierarchical fuzzy systems (HFSs) for early diagnosis of heart disease. However, to design the HFSs is challenging, especially for the complex system. Therefore, in this paper, we foc us on designing a hierarchical fuzzy system to handle the complex medical application. The designed HFS consists of six key main steps implemented on heart disease. The input variables of heart disease includes shortness of breath, discomfort, pressure, he aviness, or pain in the chest, arm, or below the breastbone, fatigue, nausea, difficulties in climbing stairs, swelling in ankles, difficulty to sleep at night, irregular heartbeats, fullness, sweating, take frequent break during the day, dizzy and depress ed. Additionally, the output of heart disease is to classify whether the patient is healthy or suspecting with heart disease. The study contributes to providing insight into a way of designing the HFSs, particularly for the complex medical application

Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decision

A New Method for Solving Supervised Data Classification Problems

Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms

An intelligent fault diagnosis method using variable weight artificial immune recognizers (V-AIR)

The Artificial Immune Recognition System (AIRS), which has been proved to be a successful classification method in the field of Artificial Immune Systems, has been used in many classification problems and gained good classification effect. However, the network inhibition mechanisms used in these methods are based on the threshold inhibition and the cells with low affinity will be deleted directly from the network, which will misrepresent the key features of the data set for not considering the density information within the data. In this paper, we utilize the concept of data potential field and propose a new weight optimizing network inhibition algorithm called variable weight artificial immune recognizer (V-AIR) where we replace the network inhibiting mechanism based on affinity with the inhibiting mechanism based on weight optimizing. The concept of data potential field was also used to describe the data distribution around training samples and the pattern of a training data belongs to the class with the largest potential field. At last, we used this algorithm to rolling bearing analog fault diagnosis and reciprocating compressor valves fault diagnosis, which get a good classification effect

The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining

Current classification approaches usually do not try to achieve a balance between fitting and generalization when they infer models from training data. Such approaches ignore the possibility of different penalty costs for the false-positive, false-negative, and unclassifiable types. Thus, their performances may not be optimal or may even be coincidental. This dissertation analyzes the above issues in depth. It also proposes two new approaches called the Homogeneity-Based Algorithm (HBA) and the Convexity-Based Algorithm (CBA) to address these issues. These new approaches aim at optimally balancing the data fitting and generalization behaviors of models when some traditional classification approaches are used. The approaches first define the total misclassification cost (TC) as a weighted function of the three penalty costs and their corresponding error rates. The approaches then partition the training data into regions. In the HBA, the partitioning is done according to some homogeneous properties derivable from the training data. Meanwhile, the CBA employs some convex properties to derive regions. A traditional classification method is then used in conjunction with the HBA and CBA. Finally, the approaches apply a genetic approach to determine the optimal levels of fitting and generalization. The TC serves as the fitness function in this genetic approach. Real-life datasets from a wide spectrum of domains were used to better understand the effectiveness of the HBA and CBA. The computational results have indicated that both the HBA and CBA might potentially fill a critical gap in the implementation of current or future classification approaches. Furthermore, the results have also shown that when the penalty cost of an error type was changed, the corresponding error rate followed stepwise patterns. The finding of stepwise patterns of classification errors can assist researchers in determining applicable penalties for classification errors. Thus, the dissertation also proposes a binary search approach (BSA) to produce those patterns. Real-life datasets were utilized to demonstrate for the BSA