Using Predictive Analytics to Detect Hypertension with Machine Learning
Varsharani T. Dond ; Poonam N. Kale ; Prapti Gaikwad
1&2 Assistant Professor, PVG’s College of Science and Commerce, Pune,Maharashtra, India.
3 Student, PVG’s College of Science and Commerce, Maharashtra, India.
Email_Id: varshadond14@gmail.com1, poonamkale2730@gmail.com 2
ABSTRACT
Hypertension, commonly referred to as high blood pressure, is a leading cause of cardiovascular diseases and contributes significantly to premature deaths worldwide. Despite advances in medical treatments, efforts to control hypertension globally have not been fully effective, especially in low- and middle-income countries (LMICs), where access to healthcare resources is limited. A major challenge in managing hypertension is its often undetectable nature; the condition frequently presents no symptoms, leaving individuals unaware of it until serious health issues arise. However, early detection can have a profound impact by preventing complications and reducing both health risks and the financial burden on healthcare systems.
This research explores the use of machine learning techniques to detect hypertension, leveraging the power of algorithms to analyse large datasets, identify patterns, and make predictions based on various clinical and lifestyle factors. By examining patient data—including blood pressure readings, demographic information, and lifestyle habits—our goal is to develop a model that can predict the onset of hypertension at an early stage. This approach has the potential to improve early detection rates and contribute to better public health outcomes, particularly in resource-limited settings.
The primary objective of this project is to develop a machine learning-based tool for early hypertension detection, aiding healthcare providers in making accurate decisions and taking prompt action. This system could play a crucial role in reducing the global burden of hypertension, particularly in LMICs, by enhancing screening efforts and ensuring individuals at risk receive timely and appropriate care.
Keywords : Hypertension, High blood pressure, Undetectable nature, Machine learning, Predictive model, Clinical data, Machine learning algorithms, Health risks
