José Carlos R. Alcantud

Glaucoma Diagnosis: A Soft Set Based Decision Making Procedure

Glaucoma is one of the main causes of blindness in the world. Until it reaches an advanced stage, Glaucoma is asymptomatic, and an early diagnosis improves the quality of life of patients developing this illness. n nIn this paper we put forward an algorithmic solution for the diagnosis of Glaucoma. We approach the problem through a hybrid model of fuzzy and soft set based decision making techniques. Automated combination and analysis of information from structural and functional diagnostic techniques are used in order to obtain an enhanced Glaucoma detection in the clinic.

Incomplete Soft Sets: New Solutions for Decision Making Problems

Alcantud and Santos-Garcia [2] revisit the soft set based decision making problem under incomplete information. Their solution relies on a classical Laplacian argument from probability theory. In view of the computational characteristics of such algorithm, we propose two related solutions that efficiently evaluate problems with many more incomplete data. A computational analysis assesses the performance of our algorithms and compares them with earlier solutions in the literature.

Fuzzy Soft Set Decision Making Algorithms: Some Clarifications and Reinterpretations

We do two things in relation with fuzzy soft set decision making in this paper. Both in the score-based and fuzzy choice values approaches to decision making, the modifications that account for the model with positive and negative attributes are put forward and discussed for the most common fuzzy negation. We also provide a reinterpretation of the fuzzy choice values solution in terms of choice values associated with fuzzy opportunity costs.

Expanded hesitant fuzzy sets and group decision making

We define expanded hesitant fuzzy sets, which incorporate all available information of the decision makers that provide the membership degrees that define a hesitant fuzzy set. We show how this notion relates to hesitant fuzzy set and extended hesitant fuzzy set. We define various scores for this setting, which generalize popular scores for hesitant fuzzy elements. Finally, a group decision making procedure is presented and illustrated with an example.

An Adaptive Soft Set Based Diagnostic Risk Prediction System

Recently, risk based prediction models in medical diagnostic systems gain wider significance in deciding most appropriate diagnostic treatments and for clinical usage. Prostate cancer is a disease which is difficult to diagnose and there are number of failure cases reported. Therefore, an effective and aggressive selection of multiple factors influence on the disease is required. In this paper, an adaptive soft set based diagnostic risk prediction system is presented with the implementation on prostate cancer. The system receives input parameters related to the disease and gives out the risk percentage of the patient. Soft sets are generated with the input parameters by fuzzification followed by rule generation. The risk percentage of the rules are individually calculated for Precision, Recall and F-Measure, that conclude on the best risk percentage based on the maximum area under the curve (AUC) in each case. This ensures to select the most influential risk parameters in treating the disease. Specificity and sensitivity of the test system yield 75.00% and 45.45% respectively.

A social choice approach to graded soft sets

We establish a correspondence between ideas from soft computing and social choice. This connection permits to draw bridges between choice mechanisms in both frameworks. We prove that both Soft sets and the novel concept of Graded soft sets can be faithfully represented by well-established voting situations in Social Choice. To be precise, their decision making mechanism by choice values coincides with approval voting and the Borda rule respectively. This analysis lays the basis for new insights into soft-set-inspired decision making with a social choice foundation.

A Linguistic Approach for Self-Perceived Health State: A Real Study for Diabetes Disease

The concept of life quality is a subjective feeling that only patient is able to define. The absence of disease is one of the determinants of well-being and life quality. Generally, self-perceived health status is measured by specific or generic questionnaires. The health information collected in the questionnaires is usually expressed by numerical values although the indicators evaluated are qualitative and subjective. This contribution proposes a linguistic approach where health information provided by patients is modelled by means of linguistic information in order to manage the uncertainty and subjectivity of such assessments. The contribution introduces a new model for measuring self-perceived health that can manage linguistic information and computes a final linguistic evaluation for each patient, applying an effective aggregation operator. A real case study is also presented to show the usefulness and effectiveness of the proposed model in the case of diabetes disease.