Abstract :
The heart pumps the blood around the body to supply energy and oxygen for all the tissues of the body. In order to pump the blood, heart pushes the blood against the walls of arteries, which creates some pressure inside the arteries, called as blood pressure (BP). If this pressure is more than the desired level, we treat it as high blood pressure (HBP). Present days, HBP victims are growing in number across the globe. BP may be elevated because of change in biological or psychological state of a person. Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body’s vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. This project presents a new study based on a machine learning technique, specifically on Random Forest Classification, for predicting blood pressure. The study was carried out using a dataset containing a variety of variables/factors. Machine Learning (ML) could also be a kind of AI (AI) supported pattern recognition. In ML, a given data set called “training data” is used for performing predictions without explicit programming, and it’s now become a useful gizmo in medical research. ML may involve learning algorithms and unsupervised, reinforced learning or feature learning or creation of other prediction models High sign (BP) could also be a serious risk factor for cardiovascular diseases (CVD).
Project Output Video :
Algorithem/Model
Random Forest Classification.
System Requirements :
HARDWARE REQUIREMENTS :
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB.
SOFTWARE REQUIREMENTS :
- Operating system : Windows 10 / 11.
- Coding Language : Python 3.8.
- Web Framework : Flask.
- Frontend : HTML, CSS, JavaScript.