FEATURE LEARNING AND ANALYSIS OF PRE EXISTING CONDITIONS PRONE TO COVID VIRUS DURING SECOND WAVE

Dr. Supriya S; SHAMI K; SHARMISHTA C; Sowjanya  V; Aneja P; Krathiyayini J

FEATURE LEARNING AND ANALYSIS OF PRE EXISTING CONDITIONS PRONE TO COVID VIRUS DURING SECOND WAVE

Authors: Dr. Supriya S, SHAMI K, SHARMISHTA C, Sowjanya V, Aneja P , Krathiyayini J

Unique Paper ID: 152431
Volume: 8
Issue: 3
PageNo: 378-380

Keywords: No Keywords Found

Abstract:
The major concern for Covid is prevention of spread. It can only be done by fast testing and isolation. But, the fastest test takes two hours by which majority of the spread takes place. We have made a website which takes a dataset from hospital and predicts if the patient is covid positive or not. The model uses the vast Covid-19 test dataset which has various vitals and symptoms along with the patientâ€™s history of pre-existing medical conditions and Covid-19 test results as its features. We have trained the dataset using random forest classifier algorithm as it has the maximum accuracy in predicting the patientâ€™s final result. This trained model is then used for predicting if the patient is potentially Covid positive or negative based on the input provided from the user. For this purpose we have desiged UI as follows. We are using the technologies like Python, Flask framework for predicting the results, html and css code for UI integration. The objective of the added feature is to build a user-friendly application for hospitals which can be used to predict the Covid test results of the patients based on their symptoms and recorded vitals. This way the hospitals authorities can separate the potential Covid patients from the healthy crowd and stop them from infecting healthy individuals before the confirmed reports are released.

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ISSN: 2349-6002
Volume: 8
Issue: 3
PageNo: 378-380

FEATURE LEARNING AND ANALYSIS OF PRE EXISTING CONDITIONS PRONE TO COVID VIRUS DURING SECOND WAVE

Available:https://ijirt.org/Article?manuscript=152431

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8.01 (Year 2024)

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Journal no 47859

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