The systematic review of artificial intelligence applications in breast cancer diagnosis

This article belongs to the Special Issue Artificial Intelligence Technology in Clinical Classification and Prediction.Several studies have demonstrated the value of artificial intelligence (AI) applications in breast cancer diagnosis. The systematic review of AI applications in breast cancer diagnosis includes several studies that compare breast cancer diagnosis and AI. However, they lack systematization, and each study appears to be conducted uniquely. The purpose and contributions of this study are to offer elaborative knowledge on the applications of AI in the diagnosis of breast cancer through citation analysis in order to categorize the main area of specialization that attracts the attention of the academic community, as well as thematic issue analysis to identify the species being researched in each category. In this study, a total number of 17,900 studies addressing breast cancer and AI published between 2012 and 2022 were obtained from these databases: IEEE, Embase: Excerpta Medica Database Guide-Ovid, PubMed, Springer, Web of Science, and Google Scholar. We applied inclusion and exclusion criteria to the search; 36 studies were identified. The vast majority of AI applications used classification models for the prediction of breast cancer. Howbeit, accuracy (99%) has the highest number of performance metrics, followed by specificity (98%) and area under the curve (0.95). Additionally, the Convolutional Neural Network (CNN) was the best model of choice in several studies. This study shows that the quantity and caliber of studies that use AI applications in breast cancer diagnosis will continue to rise annually. As a result, AI-based applications are viewed as a supplement to doctors' clinical reasoning, with the ultimate goal of providing quality healthcare that is both affordable and accessible to everyone worldwide

Fuzzy-based PROMETHEE method for performance ranking of SARS-CoV-2 IgM antibody tests

This article belongs to the Special Issue Monitoring and Detection for SARS-CoV-2 and Its Variants.Antibody tests, widely used as a complementary approach to reverse transcriptase-polymerase chain reaction testing in identifying COVID-19 cases, are used to measure antibodies developed for COVID-19. This study aimed to evaluate the different parameters of the FDA-authorized SARS-CoV-2 IgM antibody tests and to rank them according to their performance levels. In the study, we involved 27 antibody tests, and the analyzes were performed using the fuzzy preference ranking organization method for the enrichment evaluation model, a multi-criteria decision-making model. While criteria such as analytical sensitivity, specificity, positive predictive value, and negative predictive value were evaluated in the study, the ranking was reported by determining the importance levels of the criteria. According to our evaluation, Innovita 2019-nCoV Ab Test (colloidal gold) was at the top of the ranking. While Cellex qSARS-CoV-2 IgG/IgM Rapid Test and Assure COVID-19 IgG/IgM Rapid Tester ranked second and third on the list, the InBios-SCoV 2 Detect Ig M ELISA Rapid Test Kit was determined as the least preferable. The fuzzy preference ranking organization method for enrichment evaluation, which has been applied to many fields, can help decision-makers choose the appropriate antibody test for managing COVID-19 in controlling the global pandemic

Evaluation of patients with fibrotic interstitial lung disease: Preliminary results from the Turk-UIP study

OBJECTIVE: Differential diagnosis of idiopathic pulmonary fibrosis (IPF) is important among fibrotic interstitial lung diseases (ILD). This study aimed to evaluate the rate of IPF in patients with fibrotic ILD and to determine the clinical-laboratory features of patients with and without IPF that would provide the differential diagnosis of IPF. MATERIAL AND METHODS: The study included the patients with the usual interstitial pneumonia (UIP) pattern or possible UIP pattern on thorax high-resolution computed tomography, and/or UIP pattern, probable UIP or possible UIP pattern at lung biopsy according to the 2011 ATS/ERSARS/ALAT guidelines. Demographics and clinical and radiological data of the patients were recorded. All data recorded by researchers was evaluated by radiology and the clinical decision board. RESULTS: A total of 336 patients (253 men, 83 women, age 65.8 +/- 9.0 years) were evaluated. Of the patients with sufficient data for diag-nosis (n=300), the diagnosis was IPF in 121 (40.3%), unclassified idiopathic interstitial pneumonia in 50 (16.7%), combined pulmonary fibrosis and emphysema (CPFE) in 40 (13.3%), and lung involvement of connective tissue disease (CTD) in 16 (5.3%). When 29 patients with definite IPF features were added to the patients with CPFE, the total number of IPF patients reached 150 (50%). Rate of male sex (p<0.001), smoking history (p<0.001), and the presence of clubbing (p=0.001) were significantly high in patients with IPE None of the women <50 years and none of the men <50 years of age without a smoking history were diagnosed with IPE Presence of at least 1 of the symptoms suggestive of CTD, erythrocyte sedimentation rate (ESR), and antinuclear antibody (FANA) positivity rates were significantly higher in the non-IPF group (p<0.001, p=0.029, p=0.009, respectively). CONCLUSION: The rate of IPF among patients with fibrotic ILD was 50%. In the differential diagnosis of IPF, sex, smoking habits, and the presence of clubbing are important. The presence of symptoms related to CTD, ESR elevation, and EANA positivity reduce the likelihood of IPF

Fuzzy matrix and fuzzy markov chains.

TEZ10487Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2015.Kaynakça (s. 60-62) var.xi, 63 s. : tablo ; 29 cm.Belirsiz bir ortamda sağlıklı kararlar vermede klasik mantık kuralları yetersiz kalmıştır. Bulanık mantık böyle bir durumla karşılaşıldığında daha sağlıklı kararlar verilmesini sağlayan bir sistemdir. Bu çalışmada bulanık küme mantığına dayanan bulanık matrisler ve bulanık Markov zincirleri incelenmiş olup, sonlu ve sonsuz ufka sahip belirsizsizliğin olduğu süreçlerde etkili karar vermede kullanılan yöntemler araştırılmıştır. Bunun yanında bulanık matrislerin ve bulanık Markov matrislerinin cebirsel özellikleri araştırılmıştır. Ayrıca bazı özel koşullar altında bu matrislerin yakınsama durumları incelenmiş ve bu matrisler birbirleriyle karşılaştırılmıştır.The rules of classical logic is insufficient in making healty decisions in an unclear environment. Fuzzy logic is a system that provides making more healty decisions when facing such a situation. In this work, fuzzy matrices based on fuzzy set logic and fuzzy Markov chains was examined and methods used in making effective decision which in processes that there is an uncertainty with finite and infinite horizon investigated. Furthermore, algebraical properties of fuzzy matrices and fuzzy Markov matrices have been investigated. Also, convergence of these matrices has been studied under some certain circumstances and compared with each other

Markov analysis of the fuzzy states and its economic application.

TEZ12810Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2020.Kaynakça (s. 89-94) var.XIV, 95 s. :_tablo ;_29 cm.Belirsizliğin hakim olduğu ortamlarda dinamik bir sistemin matematiksel olarak modellenmesi, analiz edilerek gelecekte belirmesi muhtemel durumlarının öngörülmesi ve mevcut anda strateji belirleme kararının verilmesi oldukça zorlu ve riskli bir süreci kapsamaktadır. Markov analizi dinamik sistemlerin modellenmesinde yaygın olarak kullanılan çok önemli bir modeldir fakat belirli durumları kapsamaktadır. Zadeh’ in 1965’ te temellerini attığı, belirsizliğin matematiksel olarak ifade edilebilmesini sağlayan bulanık mantığa dayanan bulanık küme teorisi bu tür sistemlerin modellenmesinde son zamanlarda karar bilimine önemli derecede katkı sağlamıştır. Finansal yatırım araçları belirsizliğin hakim olduğu dinamik bir sistem olan borsada işlem gören ve yüksek risk içeren araçlardır. Bu çalışmada, Amerikan Dolar Endeksi, Avro Endeksi, Japon Yeni/Dolar Paritesi ve altın gibi değerli emtia aracına ait gelecek tahmini aylık verilerden yararlanılarak bulanık durumların Markov zinciri analizi yöntemi ile analiz edilmiştir. Bunun için yeterince uzun bir dönem belirlenerek önerilen yatırım araçlarına ait geçmiş verilerden yararlanılmıştır. Elde edilen verilerdeki aylık değişim oranları bulanık durumlara ayrılarak bulanık sınıflandırma yapılmıştır. Daha sonra durumların tanımlandığı bulanık kümelerden yararlanılarak bulanık durumların olasılık geçiş matrisleri oluşturulmuştur. Son olarak rassal olarak belirlenen verilerden ve klasik Markov sürecinde olduğu gibi olasılık geçiş matrisinden yararlanılarak sistemde bir sonraki adımda meydana gelebilecek durum tahmini yapılacak ve elde edilen sonuçlar gerçekte meydana gelen durum ile karşılaştırılarak oluşturulan modelin güvenilirliği test edilmiştir. Bununla birlikte tahmini yapılacak olan finansal araçların denge durumları da incelenmiştir. Bu yatırım araçlarının birbirleri ile ilişkili olup olmadıkları da ayrıca analiz edilmiştir. Ele alacağımız örnekler, Markov zincirlerine ve Bulanık durumlu Markov zincirlerine göre değerlendirilmiş ve sonrasında bu iki sürece dayanarak optimal politikalar belirlenerek bu iki modelin farklılıkları ve benzerlikleri tartışılmıştır.Mathematical modeling of a dynamic system in environments characterized by uncertainty, analyzing the possible situations that could occur in the future, determining a strategy and making good decisions in a timely manner is a very challenging and risky process. Markov analysis is a very important model widely used in the modeling of dynamic systems, but is based on exact situations. Fuzzy set theory, which allows the mathematical expression of uncertainty based on fuzzy logic was originally defined by Zadeh in 1965 and has recently contributed significantly to the science of decisionmaking. Financial investment instruments are instruments that carry high-risk when traded on the stock exchange, which is a dynamic system of uncertainty. In this study, the fuzzy states of the Markov chain analysis method has been applied for analyzing and estimating the future of valuable commodity instruments such as the American Dollar Index, Euro Index, Japanese Yen / Dollar Parity and gold price by using their monthly data. To achieve this, a sufficiently long period was determined and historical data of the proposed investment instruments were used. The monthly change rates in the data obtained were divided into fuzzy situations and fuzzy classification was made. Then, the probability transition matrices of fuzzy states was obtained. Finally, using the randomly determined data and probability transition matrix as in the classical Markov process, the situation that will occur in the next step has been estimated and the reliability of the model has been tested by comparing the results with the actual situation. Furthermore, the stable status of the probability transition matrix of the financial instruments has been obtained and examined. The relation of these investment instruments has also been analyzed. The results of the classical Markov chain and the fuzzy states of the Markov chain have been obtained and evaluated in order to obtain the best strategy of the applications and the differences and similarities of these two models have been discussed

The capacity of Artificial Intelligence in COVID-19 response: A review in context of COVID-19 screening and diagnosis

This article belongs to the Special Issue Machine Learning Applications for COVID-19 and Its Complications: Screening, Diagnosis, Treatment, and Prognosis.Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60-70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists

Inotrope Analysis for Acute and Chronic Reduced-EF Heart Failure Using Fuzzy Multi-Criteria Decision Analysis

Heart failure is a progressive disease that leads to high mortality rates if left untreated, and inotropes are a class of drugs used to treat a type of heart failure where patients have reduced ejection fraction (HFrEF). This study aims to utilize the Fuzzy-Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), an effectively used multi-criteria decision making (MCDM) technique. To analyze the characteristics of the most often used inotropes for acute HFrEF and chronic HFrEF, we use the same parameters set with distinct importance factors and aims for each property and, therefore, mathematically demonstrate the strengths and weaknesses of each inotrope alternative. As a result, a detailed ranking list for each HFrEF class was obtained, with supplementary information on how each parameter contributed to the ranking of each inotrope. From these results, it was concluded that the F-PROMETHEE method is applicable for evaluating the risks and benefits of various inotropes to determine a starting point for treating an average patient when making a quick decision without complete patient data. As demonstrated in this study, it is possible to easily use the same data set and only change some preference parameters according to individual needs to produce patient-specific results. In this study, we showed that creating a decision-making system that mathematically assists clinical specialists with their decision-making process is feasible

Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively