• Shaif Jawed
• Dhawal Mishra
• Sangita Paul
• Sonal Borse

In recent years, Brain Tumor has become one of the most dangerous disease which is increasing at an alarming rate. To put it simply, Brain Tumor is the growth of abnormal cells in the brain. They generate stress which can lead to brain damage, so detecting a Tumor before time is necessary in order to prevent the disease from entering the last stage. The common technologies devised in order to detect Brain Tumor till now involved Magnetic Resonance Imaging (MRI) or X-Ray. There are many methods to detect Brain Tumor using MRI imaging techniques but there aren’t many methods to predict the occurrence of Brain Tumor before getting an MRI. This paper provides an easy and efficient procedure to estimate the possibility of Brain Tumor without much complexity. The proposed device makes use of a mathematical approach involving sub-symptoms, verifies the main symptom, predicts the possibility of a person having a malignant Tumor or not and finally keeps a record of patient’s data and sends a report to the respective patients via QR Code. The sub-symptoms data is acquired through a heart rate sensor, a temperature sensor and a sphygmomanometer. This equipment can be used at home, but it is especially designed for hospitals that can’t afford an MRI scanner. The proposed model demonstrates how Doctors can analyse the data generated by this equipment and give an overall feedback about the test results.

Systolic blood pressure, Diastolic blood pressure, Malignant Tumor, Brain Tumor Prediction, MRI

"Brain Tumor Prediction by Utilizing Mathematical Approach and Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.574-579, April 2020, Available :http://www.jetir.org/papers/JETIR2004575.pdf

"Brain Tumor Prediction by Utilizing Mathematical Approach and Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp574-579, April 2020, Available at : http://www.jetir.org/papers/JETIR2004575.pdf