A fuzzy knowledge based system for clinical diagnosis of tropical fever

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sıtma ve tifo Sahra-altı Afrika'nın en büyük tropikal ateş enfeksiyonlarıdır. Her ikisi de bölgenin hastalık, ölüm ve ekonomik kayıplarının sebebidir. Tifo ateşi sebebiyle, her 100.000 kişiden 725 tifo vakasına yakalanmakta ve bu hastalardan da 7 adedi ölümle sonuçlandığı tahmin edilmektedir ve Dünya'nın sıtma ölümlerinin %90'ı Sahra-altı Afrika'da meydana gelmektedir. Bu iki hastalığın teşhisinde önemli olan çok sayıda belirti bulunması ve birçoğunun da ortak olması dolayısıyla teşhis zorlaşmaktadır. Bulanık küme teorisine ve insan gibi sonuçlandırma üzerine dayanan bulunak mantık, insani bilimlerde yaygın olarak kullanılmakta ve birçok problemi başarılı bir şekilde çözmektedir. Sınıflandırma ve karar verme görevlerine ihtiyaç duyulan tıbbi teşhis bu cazip uygulamalardan biridir. Belirsizliklerin olduğu teşhis özelliklerindeki karmaşıklıklar bilgisayar sistemlerinde kullanılan doğal dil ile üstesinden gelinmiştir. Bu çalışmada, Sahra-altı Afrika'da sıtma ve tifo ateşinin klinik teşhisi için bilgi tabanlı teşhis sisteminin (TROPFEV) tasarımında bulanık mantık kullanımı anlatılmaktadır. Bilgiler, tıp uzmanları danışmanlığında Uganda Sağlıklı Bakanlığı tarafından hazırlanan UCG-2012'den (Uganda Klinik Klavuzu 2012) çıkarım yapılmıştır. Bu kaynaklardan edinilmiş bilgiler modellenip, bulanık kural tabanlı mantık kullanılarak tanımlanmış ve Matlab 2012a gerçeklenmiştir. Toplanan bilgilere göre, 21 adet teşhis özellikleri, ateş hastalığının durumuna ya da şiddetine göre sistemi oluşturmak için düzenlenmiştir. Kullanıcı, karmaşık-sıtma, karmaşık olmayan-sıtma, karmaşık-tifo, karmaşık olmayan-tifo veya bilinmeyen ateş cevabını sistemden beklemektedir. Test ve performansını değerlendirmek için, TROPFEV sistemin sonuçları ile doktor tarafından yapılan teşhis sonuçlarıyla karşılaştırılmıştır. Uzman teşhisleri ve sistem teşhisleri arasındaki % 86 oranında doğruluk olduğunu görülmüştür. Sonuç olarak, tıbbi teşhis için tecrübesiz hekimlerin teşhislerine daha hızlı ve verimli bir şekilde teşhis koyabilmek için yardımcı olması amacıyla bulanık mantık kullanımına ağırlık verilebilir.. Çünkü bulanık mantık belirtilerdeki kesin olmama sıkıntılarının üstesinden gelebilmek için bulanıklık kümelerini kullanır ve bir sınıflandırmaya ilişkilendirir.Malaria and typhoid fever are major tropical fever infections. Both are responsible for significant morbidity, mortality and economic loss in the region. Typhoid fever is estimated to cause 725 incident cases and 7 deaths per 100,000 people in the year and on the other side 90% of the total world malaria deaths occur in the Sub-Saharan Africa. The two diseases malaria and typhoid fever have several diagnosis features with overlapping signs and symptoms which are a task in medical diagnosis. Fuzzy logic that lies on the fuzzy set theory and similar to human reasoning is widely used for human-related sciences, and successfully solves many problems. Medical diagnosis is one of these attractive applications, which requires classification and decision making tasks. It uses natural language to represent data into computer systems where complications in diagnosis features such as vagueness are perfectly handled. This thesis describes the use of fuzzy logic to design a knowledge based system for clinical diagnosis of malaria and typhoid fever (TROPFEV) in Sub-Saharan Africa. Knowledge was extracted from the documentary of UCG-2012 (Uganda Clinical Guidelines 2012) prepared by the ministry of healthy in Uganda as well as consulting medical experts. The knowledge acquired from these resources is modelled, represented using fuzzy rule based reasoning and implemented in Matlab 2012 a. According to the collected knowledge, 21 diagnosis features have been organised with their situations or severity during fever infections to build the system. The user is expected to get the answer of complicated malaria, uncomplicated malaria, complicated typhoid, uncomplicated typhoid or unknown fever. For testing and evaluating its performance, the results of the TROPFEV system were compared with the results of diagnosis made by a real doctor The difference in results between expert diagnosis and system diagnosis showed that the expert system have similarity with the real experts with 86% accuracy. In conclusion, the use of fuzzy logic in medical diagnosis can be emphasized because it provides an efficient way to assist inexperienced physicians to arrive at the final diagnosis of fever more quickly and efficiently. This is because fuzzy logic applies fuzzy sets to handle vagueness existing in symptoms

Development of fuzzy logic-base diagnosis expert system for typhoid fever

Typhoid fever (TyF), caused by salmonella typhoid bacteria, represents one of the main public health challenge in various parts of the world. It is often treatable when diagnosed early, but if left untreated could lead to other medical complications. This study proposed an artificial intelligence means (arim) for diagnosis of TyF. The objectives are to find out the leading risk factors for TyF, develop fuzzy logic base-expert system, called Typhoid Responsive Expert System (TyRes), that can predict the ailment from symptoms and use TyRes to predict TyF in patients. Two sets of questionnaires were used for data collection. 325 copies were administered to the patients in 25 hospitals in Lagos, Abeokuta and Ifo, South-west Nigeria. Another set of 200 copies were administered to human medical experts (hme), 70 doctors and 140 qualified nurses, to capture hme knowledge about TyF and its symptoms. The data was analysed using Chi-Square to identify the main symptoms spotted by most of the hme. TyRes was implemented in Matlab 2015a using the main factors as input variables. Vomiting, high-temperature, weakness, abdominal-pains and loss-of-appetite were the input variables used to develop TyRes. When tested to predict TyF in 25 patients, 76% accuracy was derived when comparing hme predictions with TyRes results. It can be concluded that TyRes can mimic hme by 76% of all TyF predictions. The arim is considered reliable and can be used at home, school and health centres where hme are scarce

Impute the Missing Data through Fuzzy Expert System for the Medical Data Diagnosis

Data Processing with missing attribute values based on fuzzy sets theory. By matching attribute-value pairs among the same core or reduce of the original data set, the assigned value preserves the characteristics of the original data set. Malaria represents major public health problems in the tropics. The harmful effects of malaria parasites to the human body cannot be underestimated. In this paper, a fuzzy expert system for the management of malaria (FESMM) was presented for providing decision support platform to malaria researchers, The fuzzy expert system was designed based on clinical observations, medical diagnosis and the expert�s knowledge. We selected 15 cases with Malaria and computed the missing results that were in the range of common attribute element by the domain experts

Fuzzy logic: A “simple” solution for complexities in neurosciences?

Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences

Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

A Fuzzy Intelligent Framework for Healthcare Diagnosis and Monitoring of Pregnancy Risk Factor in Women

The harmful effect of pregnancy risk factors to the body cannot be underestimated. Pregnancy risk factors are all the aspects that endanger the life of the mother and the baby. The infant mortality rates are still high in developing countries despite national and international efforts to redress this problem of pregnancy risk factors. The operations of the prediction of pregnancy risk factors are complex and risky due to fluctuation in the diagnosis of these risk factors. This is due to the vagueness, incompleteness, and uncertainty of the information used. Also, the health population index, which is based primarily on the result of medical research, has a strong impact upon all human activities. Medical experts are considered best fit for interpretation of data and setting the diagnosis, but medical decision making becomes a very hard activity because the human experts, who have to make decision, can hardly process the huge amount of data. This paper presents a fuzzy logic model for the diagnosis and monitoring of pregnancy risk factor for in order to make accurate reasoning with huge amount of uncertain knowledge. The model is developed based on clinical observations, medical diagnosis and the expert’s knowledge. Twenty-five pregnant patients are selected and studied and the observed results computed in the range of predefined limit by the domain experts. The model will provide decision support platform to pregnancy risk factor researchers, physicians and other healthcare practitioners in obstetrical. The study will also guide healthcare practitioners in obstetrical and gynecology clinic regions in educating the women more about the pregnancy risk factors and encouraged them to start antenatal clinic early in pregnancy. Keyword: Fuzzy inference System, Artificial Intelligence, Expert System, Pregnancy risk factors, Infant mortality, Pregnancy outcom

Towards a global One Health index: a potential assessment tool for One Health performance

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge

Towards a global One Health index: a potential assessment tool for One Health performance

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8–65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-022-00979-9