Cardiovascular diseases (CVDs) are the leading cause of death globally. However, most CVD-related diseases are known to be completely curable once diagnosed in their early stages. In this case, healthcare providers must place a greater emphasis on heart failure diagnosis and prediction.
By evaluating a patient’s health record, emerging novel strategies for data analysis may enable early diagnosis of CVD. The arteriolar-venular ratio (AVR) was used as a marker for early detection of cardiovascular diseases. A narrower CRAE, wider CRVE and a lower AVR have been associated with increased cardiovascular events. Retinal fundus photography proposed for stroke risk assessment due to its similarity between retinal and cerebral microcirculations.
Problem Definition: “Cardiac Risk Prediction with Retinal Fundus Imaging Using AL/ML Techniques”.
Objective 1: To Achieve high accuracy in predicting heart health status from RF images.
Objective 2: To Provide speedy and accurate diagnose system for real time usage.
Objective 3: To Develop an eco-system consisting of real-time data sensors, ML based prediction systems and doctors to help a large population of high-risk, believed-healthy persons to make consult doctor before the situation becomes irreversible.
