Future Aspirations

In the future, we plan to use a neural network to accurately determine the weightage values for each principle factor and their relation to heart disease, as well as a supervised, linear regression machine learning algorithm to strengthen our weightage values for the sub-factors. We also plan to use age, weight, race, sex, height, BMI, and exercise details to predict the blood pressure, resting heart rate, etc. the person with that categorization would have if they have heart disease. We would then ask the patient for his/her actual blood pressure, resting heart rate, etc. and compare that to the predicted values. Using the weightage found initially, we would finally determine a confidence value of how close one is to getting heart disease. This is rather than consolidating all the values into one predictive model. One of our main goals with this product that we have yet to implement specificities in exactly what cardiovascular diseases a patient is more susceptible to. A unique feature we haven’t yet seen implemented and plan to achieve is rather than using generalized cardiovascular disease data, we plan to utilize data from each individual disease to give a personalized confidence value, and then average the confidence values to give a final, generalized susceptibility percentage for cardiovascular diseases in general. If one’s value indicates he/she is susceptible to heart disease, the website would offer tips on what he/she can do to avoid it.