Ischemic Heart Disease is a death-defying cardiovascular disease that disrupts the circulation of blood owing to plaques such as fats, cholesterol, calcium, and other substances resident in the blood, which coagulates hence narrowing the arteries. The symptoms of this disease are angina, sweating, nausea, breathlessness, heart attack, vomiting, pains of back, jaw, shoulder, arm, back, and neck, just to name but a couple. According to World Health Organization, the leading cause of death which has caused over 17 million untimely deaths of patients since 2015 to date especially individuals below the age of 70 years is Ischemic Heart disease which is a form of Cardiovascular Disease; where 81% of these recorded deaths occurring in individuals in respective of sex, particularly in low and middle-income countries of Sub-Sahara Africa. Nevertheless, in the recent past, a couple of systems have been developed to detect this non-transmittable ailment. Yet, they delivered a ton of bogus negative during testing and could not distinguish Ischemic Heart Disease given its covering symptoms it imparts to other Cardiovascular Diseases. Consequently, there was the need to proffer a solution for the issue of under-diagnosis and misdiagnosis of Ischemic Heart Disease, which is much uncontrolled in Sub-Sahara Africa. Hence, in this paper, we proposed and built up a model to predict Ischemic Heart Disease and Cardiovascular diseases using an AI technique called Bayesian Belief Network. The model was structured using Bayes Server and tested with data retrieved from the UCI Machine Learning Repository. The model had an overall prediction exactness of 99.99%; 98.86% and 99.66% sensitivity of Ischemic Heart Disease and Cardiovascular Diseases correspondingly.

Solomon Osarumwense Alile."An Ischemic Heart Disease Prediction Model Based on Observed Symptoms Using Machine Learning ". International Journal of Computer Engineering In Research Trends (IJCERT), ISSN:2349-7084, Vol.7, Issue 9,pp.9-22, September - 2020, URL:https://ijcert.org/ems/ijcert_papers/V7I902.pdf,

[1] M.A. Zoroddu, J. Aashet, G. Crisponi, S. Medici, M. Peana and V.M. Nurchi, “The Essentials Metals for Humans: A Brief Overview”. 
Journal of Inorganic Biochemistry. Vol. 195(1), pp. 120-129, 2019. DOI:10.1016/j.jinorgbio.
[2] C.M. Jones, S. M. Baker-Groberg, F.A. Cianchetti, J. J. Glynn, L.D. Healy, W.Y. Lam, J.W. Nelson, D.C. Parrish5, K.G. Phillips, D.E. Scott-Drechsel, I.J. Tagge, J.E. Zelaya, M.T. Hinds, O.J.T. McCarty, “Measurement in the Circulatory System”. Cell Molecular Bioengineering Journal. Vol.7(1), pp. 1-14, March, 2014.
[3] G.D. Buckberg, N.C. Nanda, C.Nguyen  and M.J. Kocica, “What Is the Heart? Anatomy, Function, Pathophysiology, and Misconceptions”.  Journal of Cardiovascular Development and Disease, Vol.5 (33), pp. 1-29, 2018. DOI: 10.3390/jcdd5020033.
[4] S.Mendis, P.Puska and B.Norrving, “Global Atlas on Cardiovascular Disease Prevention and Control” (1st edition.). Geneva: World Health Organization in Collaboration with the World Heart Federation and the World Stroke Organization. pp. 3–18,  2011. ISBN 9789241564373.
[5] World Health Organization, “Report on Cardiovascular Diseases: The Problem”. pp. 1-2, 17 May, 2017	.
[6] I.D. Apostolopoulos, P.P. Groumpos, “Non - Invasive Modelling Methodology for the Diagnosis of Coronary Artery Disease using Fuzzy Cognitive Maps”. Computer Methods in Biomechanics and Biomedical Engineering, pp.1-9, 2020. DOI:10.1080/10255842.2020.1768534.
[7] A. Adeli, M. Neshat, “A Fuzzy Expert System for Heart Disease Diagnosis”. Proceedings of International MultiConference of Engineers and Computer Scientists (IMECS), Hong Kong. Vol.1, March 17-20, 2010. 
[8] A.V.S. Kumar, “Diagnosis of Heart Disease using Fuzzy Resolution Mechanism”. Journal of Artificial Intelligence: Vol.5(1), pp.47-55, 2012.
[9] S.S.Sikchi, S.Sikchi, M.S. Ali, “Design OF Fuzzy Expert System For Diagnosis Of Cardiac Diseases”. International Journal of Medical Science and Public Health, Vol.2 (1), pp. 56-61, 2013. DOI: .5455/ijmsph.2013.2.56-61
[10] P.K. Anooj, “Clinical Decision Support System: Risk Level Prediction of Heart Disease Using Weighted Fuzzy Rules”. Journal of King Saud University – Computer and Information Sciences, Vol.24, pp. 27–40, 2012.
[11] S.Kumar, G.Kaur, “Detection of Heart Diseases using Fuzzy Logic”. International Journal of Engineering Trends and Technology (IJETT), Vol.4 (6), pp.2694-2699,June 2013. ISSN: 2231-5381.
[12] R.Chitra, V.Seenivasagam, “Heart Attack Prediction System Using Fuzzy C Means Classifier”. IOSR Journal of Computer Engineering (IOSR-JCE), Vol.14 (2), pp. 23-31, 2013. e-ISSN: 2278-0661, p-ISSN: 2278-8727.
[13] K.K. Oad, X.DeZhi, P.K.Butt, “A Fuzzy Rule based Approach to Predict Risk Level of Heart Disease”. Global Journal of Computer Science and Technology, Vol.14(3), Version 1,2014.
[14] J.Kim, J.Lee,Y. Lee, “Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree”. Healthcare Informatics Research, Vol. 21(3), pp.167-174, July, 2015. http://dx.doi.org/10.4258/hir.2015.21.3.167, pISSN 2093-3681 • eISSN 2093-369X.
[15] N.Pathania, Ritika, “Implementation of Fuzzy Controller for Diagnose of Patient Heart Disease”. IJISET - International Journal of Innovative Science, Engineering & Technology, Vol.2(4),pp.694-698, April 2015.
[16] R.A.Mohammadpour, S.M. Abedi, S.Bagheri, A.Ghaemian, “Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease”. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, Vol.2015, Article ID 564867, pp.1-8, 2015. http://dx.doi.org/10.1155/2015/564867 
[17] A.M Sajiah, N.A. Setiawan, O.Wahyunggoro, “Interval Type-2 Fuzzy Logic System for Diagnosis Coronary Artery Disease”. Communications in Science and Technology Journal, Vol.1 (2), pp.51-55, 2016.
[18] K.H. Miao, J.H. Miao, G.J. Miao, “Diagnosing Coronary Heart Disease Using Ensemble Machine Learning”. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7(10), pp.30-39, 2016.
[19] W.M. Baihaqi, N.A. Setiawan, I. Ardiyanto,  “Rule Extraction for Fuzzy Expert System to Diagnose Coronary Artery Disease”, 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, pp.136-141, 2016.
[20] T. Kasbe, R.S. Pippal, “Design of Heart Disease Diagnosis System using Fuzzy Logic”. IEEE, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017), pp. 3183-3187, 2017.
[21] T. Kasbe, R.S. Pippal, “Enhancement in Diagnosis of Coronary Artery Disease using Fuzzy Expert System”. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Vol.3 (3), pp.1324-1331, 2018. ISSN : 2456-3307.
[22] L.Verma, S.Srivastava,V.Sapra, “Machine Learning Methods in Early Diagnosis of Coronary Artery Disease”. Journal of Cardiology and Cardiovascular Therapy, Mini Review, Vol.11 (1), pp.1-3, May, 2018. DOI: 10.19080/JOCCT.2018.11.555801.
[23] M.S. Mahdi, M.F. Ibrahim, S.M. Mahdi, P. Singam, A.B. Huddin, “Fuzzy Logic System for Diagnosing Coronary Heart Disease”. International Journal of Engineering & Technology, Vol. 8 (1.7), pp.119-125, 2019.
[24] D. Swain, S.K. Pani, D. Swain, “An Efficient System for the Prediction of Coronary Artery Disease using Dense Neural Network with Hyper Parameter Tuning”. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol.8 (6S),  pp. 689-695, April, 2019. ISSN: 2278-3075
[25] A. Dutta,  T. Batabyal,  M. Basu, S.T. Acton,  “An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction”. Elvesier Journal Expert Systems with Applications, Vol. 159 (113408), pp. 1-37. 21 May, 2020. 
[26] A. Simon, M.S. Deo, S. Venkatesan, D.R.R. Babu, “Machine Learning and its Applications”. International Journal of Electrical Sciences & Engineering (IJESE), Vol. 1(1), pp. 22-24, 2015.
[27] I. Ben-Gal, F. Ruggeri, F. Faltin, R. Kenett, “Bayesian Networks”. Encyclopedia of Statistics in Quality and Reliability. John Wiley and Sons, Ltd.  www.eng.tau.ac.il/bengal/BN.pdf/. pp. 1-6, 2007.
[28] Z-Alizadeh Sani Dataset, “UCI Machine Learning Repository”, Center for Machine Learning and Intelligent Systems, 2017.