COMPUTATIONAL MODELS FOR DIAGNOSING TUBERCULOSIS A SYSTEMATIC REVIEW

COMPUTATIONAL MODELS FOR DIAGNOSING TUBERCULOSIS: A SYSTEMATIC REVIEW By Oluwatosin P. Ogunbodede 1 Co-authors Boluwaji A. Akinnuwesi 2 and Benjamin S. Aribisala 2 1 Department of Computer Science and Information Technology, Bells University of Technology, Ota, Ogun State, Nigeria 2 Department of Computer Sciences, Lagos State University, Ojo, Lagos State, Nigeria

OUTLINES Introduction Problem statement Aim and Objectives Research Methodology Results Taxonomy of TB Computational Diagnostic Model Areas of weaknesses Conclusion and future work

Introduction Tuberculosis (TB) is an infectious disease. Pathogen Mycobacterium tuberculosis. TB infection Mortality rates - around 1. 5 million people/year [1]. About 95% of its victims are from Africa [1]. TB disease is categorized in 3 ways. Immune system (latent or active TB) [2]. Body part infected (pulmonary-lungs and extra-pulmonary TB-spine, kidney etc. ) Impact area (TB meningitis, TB lymphadenitis, MDR-TB and TB gastrointestinal) 1. WHO, Global tuberculosis report 2013 2. Voskuil et al. M, Mycobacterium tuberculosis gene expression during adaptation to stationary phase and low-oxygen dormancy. 2004

Risk factors Internal risk factors People who have been previously infected with TB HIV/AIDS –Weak immune system [3] Those who are lower than five (5) years old [3] Diabetes (Common comorbidity with TB) worsens glycaemic control External risk factors Poor nutrition Tobacco smoking and alcohol abuse Sharing utensils with sufferers 3. Control, C. f. D. and Prevention, National centre for TB prevention. 2009

Problem Statement Difficulty in diagnosis - either asymptomatic or latent TB Negligence in Nigeria and Sub-Saharan Africa. Need for a more robust computational system for TB diagnosis as a result of slow speed and accuracy of current ones.

Aim and Objectives Aim v. This research focused on systematic review and analysis of computational models for diagnosis of TB. Objectives v. Analysis of the strengths and weaknesses of existing computational TB diagnostic models. Overall goal v To develop a more accurate computational system for TB diagnosis

Methodology flow diagram Search strategy start Identify keywords Computational, Diagnosis, Tuberculosis. Determine synonyms for major keywords Computational, Soft computing, Fuzzy Logic, et al Establish exclusion criteria Apply Boolean operators Drug, Animal, Imaging, MRI, "X ray", Radiography Diagnosis intitle: Tuberculosis "Fuzzy logic" | "Neural Network" -Animal imaging

Additional records identified through other sources (n = 0) Screening Records screened after duplicates removed (n = 256) Records excluded (n = 183) Full-text articles accessed for eligibility (n = 73) Included Records identified through database searching (Google scholar) (n = 303) Eligibility Identification Preferred Reporting Items for Systematic Reviews and Meta. Analysis (PRISMA) diagram [4] Studies included in qualitative synthesis (n = 42) Excluded papers (n = 31) No information on outcomes of interest = 18 Empirical data only = 13 4. Moher, D. , et al. , Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. 2009.

Results: Categorization of the 42 studies Initial search - 303 Only 42 studies met inclusion criteria. Artificial Neural network or neuro-fuzzy – 19 studies Expert System – 2 studies Fuzzy logic/hybrids – 7 studies Bayesian/data mining – 7 studies Genetic Algorithm/Hybrid – 5 studies Other methods – 2 studies

d ite Un a in Ch y Tu rk e m do ng Ki az il Br a bi lo m Co ge ria Ni di a In A ia US es do n In Number of papers Country of publication of reviewed papers 12 10 8 6 4 2 0

Results contd. : Study levels of evidence • • • D 1 - Paper validating group studies D 2 - Exploratory group studies or meta-analysis D 3 - Diagnostic case control study D 4 - Study of diagnostic result D 5 - Studies not directly related to population Number of papers 35 30 25 20 15 10 5 0 D 1 D 2 D 3 D 4 D 5

Taxonomy of TB Computational Diagnostic Models Artificial neural network Strengths Availability of multiple training algorithms It detects complex non-linear relationships between dependent and independent variables. Weakness Training is based on reasonable amount of previous data [5] It depends on input of experts which might be emotional Solution interpretation could be vague and can only be used to induce physician’s final decision [6, 7]. 5. Nauck, D. et al. A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error back propagation, 1993. 6. Er, O. et al. Tuberculosis disease diagnosis using artificial neural networks. 2010. 7. Amato, F. , et al. , Artificial neural networks in medical diagnosis. 2013.

Fuzzy logic/Hybrid based models Strength It has ability to deal with non-linear entities and uncertainties. Weakness They work only if the knowledge about the solution is known and it is expressed using rule based format [8, 9]. The derivation of membership functions (MF) for the fuzzy sets is complex. Identification of the most suitable defuzzification process [10] Use of linguistic variables only [11] etc. 8. Kumari, N. et al, Comparison of ANNs, Fuzzy Logic and Neuro-Fuzzy Integrated Approach for Diagnosis of Coronary Heart Disease: 2013. 9. Godil, S. , et al. , Fuzzy logic: A" simple" solution for complexities in neurosciences, 2011. 10. Amini, A. et al. Proposing Two defuzzification Methods based on Output Fuzzy Set Weights. 2016 11. Sikchi, S. Sikchi, and M. Ali, Fuzzy expert systems (FES) for medical diagnosis. 2013

Bayesian/data mining based models Strength It allows us use priors in regularization Weakness Works based on probability theory and hence gives approximate results. Both the BN and DM models were limited by test data [12, 13], thus result precision is not a certainty. 12. Saputra, R. A. , PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENYAKIT TUBERCULOSIS (TB). 2016 13. Uçar, T. et al Predicting the existence of mycobacterium tuberculosis infection by Bayesian Networks and Rough Sets. 2010

Genetic Algorithm/Hybrid based models Strength It allows the use priors in regularization Weaknesses They work based on internal rules, and therefore, can not be used in loosely defined problems. It follows the Darwinian principles of the survival of the fittest -complex, non-linear model. Accuracy is an issue of concern. [14] generated 61% accuracy on completion and few data sets used [14, 15, 16, 17]. 14. Omisore, M. O. et al. A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis. 2015 15. Asha, T. , et al. Optimization of association rules for tuberculosis using genetic algorithm. 2014. 16. Walia, N. et al. Design and identification of tuberculosis using fuzzy based decision support system. 2015 17. Elveren, E et al. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. 2011

Expert system based model Strengths Flexible knowledge base- extendable and updatable They can contain a very large amount of data Weaknesses Limited to information supplied by experts - might be vague and inconclusive. Inferences by experts might be emotionally induced or subject to correction [18]. Limited test data - might not generate the best return on output [18, 19]. 18. Hossain, et al. A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty. 2017 19. Osamor, V. C. et al. Tuberculosis–Diagnostic Expert System: An architecture for translating patients information from the web for use in tuberculosis diagnosis. 2014

Areas of weaknesses in existing computational models Accuracy, speed and worker safety precede over other weaknesses identified in existing computational models S/N Weaknesses 1 2 3 4 5 6 7 Accuracy Speed Worker Safety Flexibility Ease of use Specificity analysis Sensitivity analysis Frequency Percentage 12 8 14 2 3 1 2 28. 6 19. 0 33. 3 4. 8 7. 1 2. 4 4. 8

Limitation, conclusion and future direction Conclusion Accuracy and speed need to be improved and further quantitative analysis might be required. Also, there is the need for a more robust computational system. Future works will include Quantitative analysis to substantiate and prove the quantity values of the speed and accuracy of the research Development of a comparative study of the computational and conventional TB diagnosis techniques.

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