Neuro-Fuzzy Approach for Diagnosing and Control of Tuberculosis

Gumpy, Jerome M.; Goni, Ibrahim; Isa, Mohammed

doi:10.5121/ijcsitce.2018.5101

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aggregated matrix is implemented into fuzzy MCDM methods. The results of a comparative analysis of both the ELECTRE I and TOPSIS methods are presented in Table (19) and the ranking for both ELECTRE I (net superior (A7)) and TOPSIS (A7) methods produce first ranking. That is because, HIV patients are considered to be the best host for the TB disease.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy ELECTRE and TOPSIS method to analyze the risk factors of tuberculosis

Ezhilarasan

Felix

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Tuberculosis (TB) is an infectious disease that mainly affects the lungs and it is caused by Mycobacterium tuberculosis bacteria. WHO reported that nearly 1.4 million people died of TB in 2019 and TB is one of the top 10 causes of death in the world. Over 95% of cases and deaths are occurring in developing countries and a lot of factors such as diabetes, immune problem, malnutrition, alcohol, active smoking, crowded places, HIV infection, air pollution, kidney diseases, cancer and tuberculosis treatment workers are linked to TB. So, it is our utmost interest is to analyze the risk factors of TB. Therefore, this present study intended to combine the salient features of ELECTRE and TOPSIS methods under a fuzzy environment to analyze the risk factors of TB. Moreover, the comparative analysis is demonstrated to find the most influencing risk factors of TB. Finally, the sensitivity analysis is also performed using the entropy method to verify the results.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Tuberculosis Association of India published all the most relevant topics related TB [18]. A Neuro-fuzzy inference system was designed for diagnosing tuberculosis [19] [20]. The Genetic-Neuro-Fuzzy Inferential method was proposed for the diagnosis of TB [21].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy ELECTRE and TOPSIS method to analyze the risk factors of tuberculosis

Ezhilarasan

Felix

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Consequently, many computer scientist / researchers have bond the design of fuzzy logic with neural network to form a hybrid system referred to as neuro-fuzzy model (Fig. 4) in order to diagnose different medical ailment such as Lassa fever [13], cells classification either as cancerous or noncancerous [17], lung disease [74], breast cancer [75], tuberculosis [76]- [78], thyroid diseases [79], heart disease diagnosis [80], multiple sclerosis [81], diagnosis of Ebola hemorrhagic fever [82], monkey pox diseases [83], disease diagnosis [84], leukemia [85], bipolar disorder [86], Alzheimer [87], malaria [88], colon cancer [89], thyroid disorder [90], and autism recognition [91].…”

Section: Neuro-fuzzy Hybrid Systemsmentioning

confidence: 99%

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

2020

View full text Add to dashboard Cite

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients.

show abstract