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Polygence Scholar2022
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Ankita Mandal

Mills E Godwin High School - CMSClass of 2024



  • "Prediction4Protection: Machine Learning Application in Early Heart Disease Prediction" with mentor Swetha (Dec. 20, 2022)

Project Portfolio

Prediction4Protection: Machine Learning Application in Early Heart Disease Prediction

Started Sept. 16, 2022

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Abstract or project description

The availability and widespread distribution of healthcare in third-world and developing countries is scarce, and the threat of unreliable medical care is only exacerbated by issues of poverty and financial instability. Even within developed countries, the rights of healthcare are often infringed upon by the burdens of inadequate scheduling and unaffordable costs for the average citizen. Without proper screening and regular medical visits, early detection and treatment of heart disease, one of the prime methods of diminishing the risks the condition carries, the numbers of people who succumb to heart diseases are at a much higher rate than what they could be with modern technology. Recognizing the potential in providing this care for everyone, this project was created with the aim of predicting heart disease using the same indicators identified by healthcare professionals. In this way, those with a family history of heart disease, those with preexisting conditions, those who do not have proper access to medical care, and those who simply yearn to assess their health will still have the ability to do so, a short assessment that has the potential of saving their life. Ankita is working on a machine learning project with applications in the medical field, specifically heart disease detection, the top killer across the world. She is using heart health data to predict if a patient has glaucoma. She will work through a traditional machine learning pipeline consisting of collecting and cleaning the data, training and tuning a model, and testing it to measure its performance. In the end, she will create a web application that will allow users to input their data and return a positive or negative CVD diagnosis in real time.