Clinical Feature-Based Validation And Calibration For Diagnosing Hypertension And Cardiovascular Diseases

Main Article Content

Dr. Saud Alharbi

Abstract

The measurement of a patient's blood pressure reading is a vital biomarker that is utilized in the diagnosis of hypertension in addition to other cardiovascular diseases. Historically, it was measured with irregular and frequently extremely painful technology that was based on a cuff, such as a sphygmomanometer. This equipment was used to monitor blood pressure. In any case, it requires the collection of a number of distinct sensors; hence, this strategy is not only expensive but also inconvenient and time-consuming. The inclusion of an advanced technique that is pioneered on machine learning is incorporated as section of the remit of this study. This compendium of research's findings lead to the development of a model that is capable of correctly predicting systolic blood pressure (SBP). Clinical and lifestyle aspects were included when assessing the model. A extensive range of alternatives are available for application in training algorithms in addition to the numerous machine learning techniques that may be employed. In mandate to determine how to increase the model's accuracy, the findings of its testing and validation were examined. In mandate to accomplish the aim of precisely identifying the SBP, research was done on all three classes of hypertension utilizing the methodology that was described.

Article Details

Section
Articles