Prediction of Covid-19 Using Fuzzy-Rough Nearest Neighbor Classification

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K.S.Padmashree, P.Velmani, S.Loghambal


Prediction refers to the process of using data and statistical or machine learning techniques to estimate or forecast future events or outcomes based on patterns and trends observed in historical data. The goal of prediction is to make accurate forecasts about what is likely to happen in the future, given what is known about past events and trends. The corona virus has created a global pandemic that significantly disrupted our daily schedule and behaviour patterns. Individuals who contract COVID-19 experience a range of symptoms, which can vary in severity. It is crucial to promptly assess the health condition of individuals affected by COVID-19 by evaluating their symptoms and obtaining essential information. . . To assist in this task, physicians rely on rapid and precise Artificial Intelligence (AI) techniques that aid in predicting patients’ mortality risk and the severity of their conditions. Early identification of a patient’s severity can help conserve hospital resources and prevent patient fatalities by facilitating immediate medical interventions. This research paper introduces an innovative approach that employs the FRNN technique to train a classifier capable of achieving remarkable accuracy in predicting the survival outcomes of COVID-19-affected people. The model is trained on 11 attributes, out of which five are the primary clinical symptoms of this fatal virus: Nasal-Congestion, cough, tiredness, runny nose, fever, sore throat, Diarrhea, and breath shortness, and the other three features are test indication, age, and gender. Our proposed approach, which employs the ENN-SMOTE algorithm to tackle the issue of imbalanced data, demonstrates remarkable effectiveness as evidenced by the experimental results.

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P.Velmani, S.Loghambal, K. (2024). Prediction of Covid-19 Using Fuzzy-Rough Nearest Neighbor Classification. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 851–858. Retrieved from