A Django Application which uses pretrained Scikit Learn models of MLP, RFC and SVM to predict life of Arteriovenous Fistula.
Millions of patients worldwide suffer from Kidney failure and require dialysis. In most cases, dialysis is started after the kidney function of the patient falls below a threshold. In this scenario the patient’s kidney is essentially non functional. In order to conduct dialysis, native arteriovenous fistulas are constructed to increase blood flow in the superficial vein. Over time, as dialysis continues, the patient may suffer from hypertension and reduced vein function leading to the collapse of the fistula. The ultrasound doppler test for checking the state of the fistula are expensive and doing it again and again is not feasible. This report proposes a system which takes as input all the factors provided in real time by the dialysis machine and then uses the same to make a prediction on the state of a fistula, saving both time and money of the patient.
A detailed report explains the entire project.
A sample report from the system is also included.
Also attached are the two papers on the project:
- Paper 1 and its certificates.
- Paper 2 and its certificates.
- Mihir Wagle - D17B - 73
- Neeraj Jethnani - D17B - 26
- Juhi Bhagtani - D17B - 07
- Aishwarya Chandak - D17B - 11
- Dr. Gresha Bhatia - Deputy HOD - Computer Engineering Department, V.E.S Institute of Technology.
- Dr. Viswanath Billa - M.D., D.M., Director - Apex Kidney Care