Downloads: 3 | Views: 21 | Weekly Hits: ⮙2 | Monthly Hits: ⮙2

Research Paper | Health and Medical Sciences | United States of America | Volume 13 Issue 11, November 2024 | Popularity: 4.2 / 10

Rate This Article!      

Predicting Health Conditions Using Machine Learning Algorithms on Chronic Diseases

Leela Prasad Gorrepati

Abstract: Chronic conditions arise due to various reasons like changes in lifestyle, lack of physical activity, ignorance of early symptoms of diseases etc. Many of such like Heart disease, Diabetes, High blood pressure could be avoided, delayed or could be handled in an efficient way. Also, these are related to each other in one way or the other. 2 out of Top 3 chronic diseases such as Heart disease and Diabetes could be handled by cautious approach. Annually huge sum of amount ranging beyond $400 Billion is being spent on the treatments of these chronic diseases, in the year 2022 the amount is equivalent to nations GDP share of 17.3% [1]. Study has shown that about 60% of US population has 1 or more chronic conditions the equivalents to about 133 Million people which is raising year over year leading and it is predicted to reach 171 million by 2030[2][3]. Despite such a huge expenditure on these chronic diseases, the death rate is also high where it accounts to 70 percent each year due to these chronic conditions [4]. Changes in lifestyle, diet, physical activity and few other incorporations in our daily routine could easily bring down healthcare expenditure along with upgrading quality of life. This would bring down the alarming death rate, the intent of this white paper is to predict the risk of heart failure that they possess.

Keywords: Chronic Conditions, Heart failure, Prediction, Diabetes, Machine Learning, Random Forest Algorithm, lifestyle, Diet, Healthcare, Chronic illness

Edition: Volume 13 Issue 11, November 2024

Pages: 1585 - 1591

DOI: https://www.doi.org/10.21275/SR241123050941

Make Sure to Disable the Pop-Up Blocker of Web Browser

Click to Cite this Article

Similar Articles