Risk Factor Hypertension Prediction Model

According to the 2020 Ministry of Health reports, the public health sector is facing an acute shortage of logistical resources and qualified competent human resources, as evidenced by the doctor-to-hospital ratio in relation to population [1]. Aside from these structural and cyclical issues, the above ratios are even lower in rural areas with low incomes. Underdevelopment is a major impediment to establishing a normal public health situation, though the Burundian government is working hard to ensure that it is at an acceptable level. Furthermore, some Burundian traditions, customs, and practices are undermining efforts to build an international-standard public health facility. Indeed, the mental state of a people (tradition, culture, and practices) has a significant impact on the fluctuation of risk factors in public health. It is determined by the socioeconomic development and sociocultural behavior of the population. This demonstrates that hypertension is a public health concern in Burundi. Unfortunately, the vast majority of people are completely unaware of the risks that high blood pressure poses to public health. High blood pressure, on the other hand, has always been a key physiological measure in medical examinations, serving as one of the most important biological markers in clinical evaluation. As a result, cardiovascular diseases caused by high blood pressure have a significant impact on mortality worldwide, particularly in Burundi. Predicting high blood pressure based on risk factors can help to reduce complications associated with this disease, which is known as a silent killer. The digital era provides a variety of tools for studying, analyzing, managing, and monitoring the risk factors that contribute to and degenerate high blood pressure. The primary goal of this work is to create a decision-making tool based on the outcomes of high blood pressure epidemic and/or pandemic predictions from sanitarian districts. The current paper work employs a prediction support tool created using linear regression methods from machine learning, one of the fields of artificial intelligence. It is especially useful for optimizing the cost function. The latter allows the predicted values to be determined and defined using the gradient descent algorithm.

Management Tools for Random Demand Inventory and Supply

A mathematical management model’s added value is obtained only after the design and implementation of a user-friendly operating and usage tool. Fol-lowing work on developing an automated inventory management system and/or supplies, a dynamic model for the rational management of product stocks was established. Its implementation aims to limit or eliminate over-stocking and/or stock depletion. The orderable quantity prediction tool based on a settable and preset time period demonstrates the added value of incorporating probabilistic mathematical principles into supply management processes. In this context, this article discusses aspects of the design and implementation of random demand management algorithms based on Mar-kov chains. The goal is to forecast the state or behavior of goods marketing company’s product stocks and to develop a user supply management inter-face. The latter’s functional application will ultimately demonstrate the ac-curacy of the model. This paper also looks at how to use Markov chains to predict the reliability of any technical device, as well as how to implement an automated system with the desired technical specifications.

Development of a Quantitative Prediction Support System Using the Linear Regression Method

The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company’s areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.