CARDIOVASCULAR DISEASES DETECTION USING ARTIFICIAL INTELLIGENCE

Abstract

The likelihood of contracting a disease rises with the size of the human population. Globally, there are numerous ailments, and one of the main issues facing Healthcare systems now lack the required technology to detect illness in patients. Cardiovascular disease, or CVD, is one such illness. Any cardiovascular, vascular, or blood vessel ailment is referred to. More people globally die from CVDs than from the WHO. More so in low- and middle-income nations. When ill, it can be quite difficult for any other cause, according for persons who live alone to contact the hospital. As a result, we created a simulation that is capable of when A sick patient notifies the hospital in writing. a simulation that is capable of. Currently, the simulation merely detects and informs the hospital about patients with cardiovascular disease. We chose to focus on heart disease detection because it's one of the worst diseases and there's a significant chance that people may pass away from it. It is a classification problem to determine if a patient has heart disease or not. Age, blood sugar, cholesterol, and many other factors are considered, and the output is then provided based on the input. We leverage both traditional machine learning and state-of-the-art deep learning techniques. The machine learning techniques include a support vector machine (SVM) with Artificial Neural Network (ANN ) , logistic Regression , and