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An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.

Aurelle Tchagna Kouanou^1,2, Thomas Mih Attia¹, Cyrille Feudjio³

¹Department of Computer Engineering, College of Technology, University of Buea, Buea, Cameroon.

Journal of Healthcare Engineering

|December 2, 2021

Summary

This summary is machine-generated.

This study developed a reliable machine learning model for COVID-19 diagnosis using blood parameters. The support vector machine algorithm achieved high accuracy, sensitivity, and specificity on clinical datasets.

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Area of Science:

Medical Informatics
Computational Biology
Machine Learning in Healthcare

Background:

Accurate and rapid COVID-19 diagnosis is crucial for effective public health management.
Clinical datasets from major hospitals provide valuable data for diagnostic algorithm development.

Purpose of the Study:

To develop and validate a supervised machine learning algorithm for reliable COVID-19 diagnosis.
To identify key blood parameters influencing COVID-19 diagnosis using machine learning.

Main Methods:

Utilized two major clinical datasets from Milan, Italy, and São Paulo, Brazil.
Applied Exploratory Data Analysis (EDA) and feature selection focusing on blood parameters.
Compared various supervised machine learning models, selecting Support Vector Machine (SVM) for its superior performance.

Main Results:

Optimized SVM achieved 99.29% accuracy, 92.79% sensitivity, and 100% specificity on the Kaggle dataset.
SVM demonstrated 92.86% accuracy, 93.55% sensitivity, and 90.91% specificity on the San Raffaele Hospital dataset.
The model's performance surpassed existing literature results for these datasets.

Conclusions:

The proposed SVM-based machine learning solution is highly reliable for COVID-19 diagnosis.
Blood parameters are significant predictors for COVID-19 diagnosis when analyzed with advanced machine learning techniques.