Maximum Value Attribute based Decision Tree and Random Forest for COVID-19 Prediction

Khurram Gulzar; Basharat Ahmad Hassan; Muhammad Abbas; Zubair Ahmad; Muhammad Anis; Hasnat Ullah

Authors

Khurram Gulzar Computer Science, Qurtuba University of Science and Information Technology, Phase 3, Hayatabad, Peshawar, 25000, KPK, Pakistan
Basharat Ahmad Hassan Lecturer at Agriculture University Peshawar
Muhammad Abbas Department of Computing, Abasyn University Peshawar, Ring Road, Peshawar, 25000, Khyber Pakhtunkhwa, Pakistan
Zubair Ahmad Department of Computing, Abasyn University Peshawar, Ring Road, Peshawar, 25000, Khyber Pakhtunkhwa, Pakistan
Muhammad Anis Department of Computing, Abasyn University Peshawar, Ring Road, Peshawar, 25000, Khyber Pakhtunkhwa, Pakistan
Hasnat Ullah Department of Computing, Abasyn University Peshawar, Ring Road, Peshawar, 25000, Khyber Pakhtunkhwa, Pakistan

Keywords:

COVID-19 Prediction, MVA, Symptoms, Rough Set Theory

Abstract

The COVID-19 pandemic emerged as one of the most disruptive global health crises of the century, affecting social and economic systems worldwide. The rapid rise in infections placed immense pressure on healthcare infrastructures, demanding fast and reliable diagnostic tools. In recent years, Machine Learning (ML) has gained considerable importance in the medical field, supporting the diagnosis of conditions such as heart failure, pneumonia, dengue, breast cancer, and diabetes. In a similar way, clinical symptoms related to COVID-19 can be utilized to support early prediction, helping limit transmission. Although ensemble learning techniques such as Decision Trees and Random Forests have shown strong predictive performance for COVID-19, they often require more time and a larger number of iterations, which can be challenging when rapid detection is needed.

This study focuses on improving the efficiency of COVID-19 prediction by integrating Rough Set Theory (RST) through the Maximum Value Attribute (MVA) approach with classical Decision Tree (DT) and Random Forest (RF) models. The objective is to reduce computation time and iterations while maintaining reliable diagnostic accuracy. The proposed method classifies patients as COVID-19 positive or negative based on eight key clinical symptoms. A dataset containing clinical records of 136,294 patients, collected from an open-source GitHub repository, was used for evaluation. Four models—DT, RF, MVA-DT, and MVA-RF—were implemented in Python using Jupyter Notebook. Standard evaluation metrics were applied to assess performance. Overall, the MVA-DT model achieved the most efficient execution, while the MVA-RF model demonstrated strong predictive capability with an accuracy of 95.82%, precision of 81.90%, recall of 59.28%, and an F1 score of 68.77%.

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