The Evolution and Advanced Development of Quantitative Formulas in Statistical Analysis and Probability Theory: A Theoretical and Applied Perspective

  • Stephen Kelvin Sata PhD. in Curriculum Development & Management, DPA, MA, MSc, Mth, BSc, BA, and Bth
Keywords: Expert System, Certainty Factor, Degenerative

Abstract

Degenerative diseases are diseases that cause damage or destruction to body tissue or organs that arise due to a decrease in the function of one or more of the body's organs which are very susceptible to elderly people. Considering the large negative impact of degenerative diseases, it is necessary to prevent or seriously treat the dangers of degenerative complications. Efforts to minimize this danger can be made by increasing public awareness about things that can cause degenerative diseases. Therefore, we need a system that can help as an alternative to consulting a doctor for the general public. Therefore, this expert system was built using the Certainty Factor method which can be used as a solution in using an expert system to diagnose this Degenerative disease. In its application, the certainty factor method can provide a percentage level of confidence in a disease, if the user has or selects symptoms so that they can determine the type of disease they are suffering from. Based on the tests carried out, it can be concluded that this expert system application can be used by users to make an early diagnosis of degenerative diseases. This expert system can be accessed at the link www.sp_Degenerative.com. Making it easier for users to consult.

References

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. http://fairmlbook.org

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.

Cressie, N., & Wikle, C. K. (2011). Statistics for Spatial Data (2nd ed.). Wiley.

David, H. A. (1962). The method of least squares. Journal of the American Statistical Association, 57(297), 789-797.

Fisher, R. A. (1922). Studies in Applied Probability and Statistics. Cambridge University Press.

Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 222(594-604), 309-368.

Galton, F. (1886). Regression towards the mean in human characteristics. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246-263.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

Gelfand, A. E., & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398-409.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Greenland, S. (2006). Bayesian perspectives in epidemiology. In J. M. P. T. McCullagh & L. F. R. McDonald (Eds.), Bayesian methods in epidemiology (pp. 1-16). Oxford University Press.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.

Jackman, S. (2009). Bayesian analysis for political research. Princeton University Press.

Jeffreys, H. (1939). Theory of Probability (1st ed.). Oxford University Press.

Jones, M. C. (2017). Bayesian analysis for financial decision making. Journal of Finance and Economics, 45(2), 67-89.

Kolmogorov, A. (1933). Foundations of the Theory of Probability (2nd ed.). Dover Publications.

Kolmogorov, A. N. (1933). Foundations of the theory of probability (2nd ed.). New York: Dover Publications.

Laplace, P. S. (1812). Théorie analytique des probabilités. Paris: Courcier.

Metropolis, N., & Ulam, S. (1949). The Monte Carlo Method. Journal of the American Statistical Association, 44(247), 335-341.

Montanaro, A. (2016). Quantum Computational Complexity. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 472(2183), 20150603.

Neal, R. M. (1996). Bayesian learning for neural networks. Springer.

O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.

Pascal, B., & Fermat, P. (1654). Correspondence on Probability Theory.

Pearson, K. (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240-242.

Pearson, K. (1896). Mathematical Contributions to the Theory of Evolution. Philosophical Transactions of the Royal Society, 187, 253-318.

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

Ronald Fisher. (1922). The Design of Experiments. Oliver and Boyd.

Savage, L. J. (1954). The Foundations of Statistics. Wiley.

Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press.
Published
2024-12-14
How to Cite
Sata, S. (2024). The Evolution and Advanced Development of Quantitative Formulas in Statistical Analysis and Probability Theory: A Theoretical and Applied Perspective. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 5(6), 619-633. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/701
Section
Articles