Anti-Hypertension Drugs Classes of Prevention and Side Effects Diseases Efficiency Analysis in Association Rule Mining Techniques

Abstract

Purpose: The Healthcare department, pharmaceutical department, Hospital and Clinical sector find out and explore the diseases of status, where it has been spreaded the communicable and non-communicable diseases among the society. The healthcare department conducting different awareness of programming about the different diseases how is affected people and how is prevented the diseases in society. Accordingly, all the healthcare awareness of information passed through the different media channel, even though high and low blood pressure is pressing public health challenges and it is recognized as the biggest contributor to the global burden of diseases. Presently people health is silently affected by blood pressure low and high level and they unable to recognize that something is amiss, high blood pressure is known as the "silent killer” and If blood pressure is excessively elevated, it may have an impact on organ damage or health issues like coronary arteries, heart valve dysfunction, diabetes, kidney diseases, heart attack and stroke this all are risk factors of blood pressure abnormal status. Hypertension diseases affected the patients need to the best prevention and feature safety. The Doctors, Pharmacist and Nurse are using Anti - hypertension drugs classes of medicines for patients. Which anti-hypertension drug classes of medicine good efficacy for patients and anti-hypertension drugs classes of medicine prevention diseases, side effects diseases knowledge is important for healthcare professional. Machine learning and Data mining knowledge discovery techniques need to understand how different classes of anti-hypertensive drugs might interact with the patient’s and medications. If the healthcare professional has access to a predictive data mining technique model, they could use this to anticipate how the patient’s condition might change over time and adjust the treatment plan proactively. This kind of analytical data mining knowledge can lead to more effective treatment and better patient outcomes. Design/Methodology/Approach: Developing machine learning concept for different anti-hypertension drugs classes of medicine efficiency analysis in hypertension prevention diseases and side effects diseases and Healthcare professional to take right decision for future adjusts treatment plan to the hypertension affected patients. Findings/Result: Orange data mining analytical tool to identify the anti-hypertension drugs classes of medicines efficacy and Doctors can take right decision to better treatment for the patients. Originality/Value: Data mining association rules of support, confidence and lift correlation analysis system helps to identified about the drug of new knowledge efficiency. Paper Type: Analytical research methods applied for analysis the different types of anti-hypertension drugs classes of association correlation efficacy in data mining machine learning system.