Home⇛International Journal of Multidisciplinary Educational Research and Innovation⇛vol. 2 no. 3 (2024)

Early Diagnosis Prediction from COVID-19 Symptoms Using Machine Learning Methods

Charlyn V. Rosales

Discipline: Education

 

Abstract:

This study proposes a new method for detecting COVID-19 using Artificial Neural Networks by analyzing a person’s current symptoms without requiring laboratory tests. The methodology utilized in this study includes the data collection of the dataset from Kaggle, then the Exploratory Data Analysis was performed for data pre-processing to attain a clean and comprehensive dataset to be used to train the prediction model. To determine the highest possible performance of the algorithm, GridSearchCV was used for hyperparameter tuning and 10-fold cross validation to optimize Artificial Neural Networks performance then a prediction model was developed using the optimal configuration. The results suggest that hidden layer sizes of (100,), (50, 100, 50), and (50, 50, 50), relu and tanh activation functions, adam solver, 0.05 and 0.0001 alpha values, and adaptive and constant learning rates were the values that achieved the best algorithm performance. Experimental results show that the developed prediction model attained 98.86% accuracy, 98.79% specificity, and 100% sensitivity. This prediction model can be utilized in applications that integrate prediction models to determine the presence of the COVID-19.



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