Multi-criteria as decisions

Knowledge discovery techniques have heavily contributed to many fields with significant success. However, with the continuous growth of data, these techniques suffer from bottlenecks in processing these data. One of the directions to hinder this effect is reducing the data dimensionality which focuses on eliminating the attributes which have no significant effect on the discovery technique accuracy. This research proposes a novel method for reducing data dimensionality. The proposed method is based on two main pillars, the first is applying the adapted Saaty method for determining the attributes’ consistency with proposing further adaptation targeting more accurate accuracy determination. The second pillar is applying the clustering techniques on the consistent attributes to eliminate the least weighted attributes in each cluster which also have the least consistent measures. The result of applying the two steps is to highlight the most significant dataset attributes. The proposed method has been successfully applied on the Gastrology dataset which attributes have been reduced from 62 attributes to 31 attributes. A set of classification techniques have been applied on the dataset to prove that the dimensionality reduction has retained the classification task accuracy, the results presented that the Random Forest algorithm had an accuracy equal 95.36% when applied to the adopted dataset.

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A Proposed Method for Minimizing Mining Tasks’ Data Dimensionality

Abstract Prediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialists. This paper proposes a prediction framework based on ML approaches to predict Hepatitis C Virus among healthcare workers in Egypt. We utilized real-world data from the National Liver Institute, founded at Menoufiya University (Menoufiya, Egypt). The collected dataset consists of 859 patients with 12 different features. To ensure the robustness and reliability of the proposed framework, we performed two scenarios: the first without feature selection and the second after the features are selected based on sequential forward selection (SFS). Furthermore, the feature subset selected based on the generated features from SFS is evaluated. Naïve Bayes, random forest (RF), K-nearest neighbor, and logistic regression are utilized as induction algorithms and classifiers for model evaluation. Then, the effect of parameter tuning on learning techniques is measured. The experimental results indicated that the proposed framework achieved higher accuracies after SFS selection than without feature selection. Moreover, the RF classifier achieved 94.06% accuracy with a minimum learning elapsed time of 0.54 s. Finally, after adjusting the hyperparameter values of the RF classifier, the classification accuracy is improved to 94.88% using only four features. 

/pdf/hepatitis-c-virus-prediction-based-on-machine-learning-11q912iu.pdf

Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt

pymcdm - The universal library for solving multi-criteria decision-making problems

pymcdm—The universal library for solving multi-criteria decision-making problems

Hamy mean operator is the modified shape of the averaging and geometric aggregation operators, used for evaluation of the collection of finite numbers of attributes into a singleton set. Hamy mean operator is massively valuable and proficient principle to operate awkward and problematic information. In this analysis, we diagnosed the theory of Hamy mean operator based on complex Pythagorean fuzzy (CPF) sets, called CPF Hamy mean (CPFHM), CPF weighted Hamy mean (CPFWHM), CPF dual Hamy mean (CPFDHM), CPF weighted dual Hamy mean (CPFWDHM) operators and evaluate their dominant and valuable results as well as properties. Further, the TOPSIS “Technique for order of preference by similarity to ideal solution” method is also very valuable, computed in this manuscript based on generalized dice similarity measures to improve the worth of the diagnosed theory. Moreover, multi-attribute decision-making (MADM) and the TOPSIS method are two well-known techniques that are used for illustrating the beneficial optimal. Using these two tools, we discovered some numerical examples based on the evaluated operators, measures, and methods to show the feasibility and accuracy of the diagnosed information. Finally, we used some existing information and try to compare it with our evaluated information using our illustrated examples and discussed their geometrical interpretations. 

TOPSIS-method based on generalized dice similarity measures with hamy mean operators and its application to decision-making process

This research presents Intelligent Decision Framework (IDF) to explore and manage cases of hepatitis c virus based on data mining approach and Fuzzy logic system. The proposed framework is produced from integration between data mining decision tree, rule based classification and fuzzy logic system. On the other hand, this study improves the predication results of Fibrosis stage by using Trapezoidal Fuzzy Number (TFN) distribution as fuzzy logical system to arrive 98.1% compared to predication results that were 92.5% by data mining decision tree model for same patients sample. Fuzzy logic system predicts disease scale of Hepatitis C Virus (HCV) for patients sample through different stages of liver disease caused by virus c. The proposed framework supports physicians and Ministry of Health (MOH) strategies for treatment to limit and control HCV infections and prevalence rate in Egypt and other countries. The extracted knowledge and information from proposed framework helps decision makers to take appropriate and better decision at appropriate time to against hepatitis c viral in world. The architecture of intelligent decision framework is designed to support physicians to investigate and present treatment for HCV cases. Also, to develop intelligent machine, health care system or robots as a physician for HCV patients in high prevalence rate countries.

Intelligent Decision Framework to Explore and Control Infection of Hepatitis C Virus

Schweizer-Sklar prioritized aggregation operators for intuitionistic fuzzy information and their application in multi-attribute decision-making

Multi Criteria Business Intelligence approach (MCBI) aims to enhancement Business Intelligence Applications (BIA) by applying Multi-Criteria Decision Making (MCDM). MCBI approach contributes to improve Business Intelligence Decision Support System (BIDSS) for BIA. Also MCBI approach presents a standard method to evaluate and select business decisions. The recommended business decision is the suitable and optimal choice to implement. The proposed model for MCBI approach that consists of five major components. The first component is business objectives, problem definition and main goals. The second component is a business heterogeneous data treatment which gathering from different resources and related with different areas. The third component is a unified business intelligence databases. The fourth component is a business intelligence processing. The fifth component is a evaluating the business decisions to select the suitable and optimal solution.

/pdf/multi-criteria-business-intelligence-approach-3onitjriyk.pdf

Mohamed M. Reda Ali

Papers

TOPSIS-method based on generalized dice similarity measures with hamy mean operators and its application to decision-making process

Intelligent Decision Framework to Explore and Control Infection of Hepatitis C Virus

Schweizer-Sklar prioritized aggregation operators for intuitionistic fuzzy information and their application in multi-attribute decision-making

Multi-Criteria Business Intelligence Approach