ABSTRACT
The idea of composition relations on Fermatean fuzzy sets based on the maximum-extreme values approach has been investigated and applied in decision making problems. However, from the perspective of the measure of central tendency, this approach is not reliable because of the information loss occasioned by the use of extreme values. Based on this limitation, we introduce an enhanced Fermatean fuzzy composition relation with a better performance rating based on the maximum-average approach. An easy-to-follow algorithm based on this approach is presented with numerical computations. An application of Fermatean fuzzy composition relations is discussed in diagnostic analysis where diseases and patients are mirrored as Fermatean fuzzy pairs characterized with some related symptoms. To ascertain the veracity of the novel Fermatean fuzzy composition relation, a comparative analysis is presented to showcase the edge of this novel Fermatean fuzzy composition relation over the existing Fermatean fuzzy composition relation.
ABSTRACT
Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
The outbreak of COVID-19 has cost the world a lot of lives and causes the shutdown of businesses which get most of the countries gone into economic recession. Despite the fact that some of the vaccines of the pandemic are now available, immediately after the first wave of the COVID-19 pandemic, the second wave of the pandemic has now started and causes a lot of lives and grounds a lot of businesses that have resumed. Therefore, in order to contain its further spread among humans, testing and screening of a large number of suspected COVID-19 cases for appropriate quarantine and treatment measures are of high priority to all governments around the world. However, most of the countries are facing inadequate and standard laboratories for testing a large number of suspected COVID-19 cases in their countries despite the fact that the virus is now endemic like other communicable diseases. Therefore, alternatives in non-medical diagnosis of COVID-19 techniques using artificial intelligence which include deep learning, data mining, machine learning, expert system, software agent, and other techniques are urgently needed in the cause of the diagnosis, containing and combatting the further spread of the pandemic. In this study, deep learning algorithms were used to develop models for predicting COVID-19 using chest x-ray images, and models were able to extract COVID-19 imagery features and provide clinical diagnosis ahead of the pathogenic test with a view to saving time, thereby complementing COVID-19 testing laboratories. ResNet50-based model was found to have the highest accuracy, sensitivity, and AUC score of 99%, 89%, and 96%, respectively. In contrast, EfficientNet B4-based model was found to have the highest specificity of 89%. Therefore, ResNet50-based model which has the highest sensitivity of 89% can be used for diagnosis of COVID-19 infection as well as an adjuvant tool in radiology department in hospitals. © 2022, Springer Nature Switzerland AG.