Open Access

ARTICLE

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

by Sirisati Ranga Swamy¹, S. Phani Praveen², Shakeel Ahmed^3,*, Parvathaneni Naga Srinivasu⁴, Abdulaziz Alhumam³

1 Department of CSE, Vignan’s Institute of Management and Technology for Women Ghatkesar, Telangana, India
2 Department of CSE, PVPSIT, Vijayawada, Andhra Pradesh, 520007, India
3 Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
4 Department of Computer Science and Engineering-AIML, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India

* Corresponding Author: Shakeel Ahmed. Email:

Computer Systems Science and Engineering 2023, 45(1), 869-886. https://doi.org/10.32604/csse.2023.029822

Received 12 March 2022; Accepted 19 May 2022; Issue published 16 August 2022

Abstract

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model’s performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

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Keywords

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