Novel Approach for Predicting COVID-19 Symptoms using ARM based APRIORI Algorithm
6th International Conference on Computing Methodologies and Communication, ICCMC 2022
; : 1577-1580, 2022.
Article
in English
| Scopus | ID: covidwho-1840252
ABSTRACT
Based on several pre-defined standard symptoms, a model that can determine the coronavirus illness as positive is developed. Guidelines for these symptoms have been issued by the World Health Organization (WHO) and India's Ministry of Health and Family Welfare. In this model the various symptoms of the illnesses is given to the system. It allows users to discuss their symptoms, with the algorithm predicting a condition based on factual information. This factual information is then evaluated using the ARM based Apriori algorithm to get the most accurate results. Other conventional models such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forests (RF) are considered and have analyzed the predictions and have found that the proposed algorithm predicts a higher accuracy score. © 2022 IEEE.
Apriori Algorithm; Feature Selection; Machine Learning; Decision trees; Diseases; Information use; Learning algorithms; Neural networks; Support vector machines; Apriori algorithms; Condition; Conventional modeling; Coronaviruses; Factual information; Features selection; Machine-learning; Random forests; Support vectors machine; World Health Organization; Forecasting
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
6th International Conference on Computing Methodologies and Communication, ICCMC 2022
Year:
2022
Document Type:
Article
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