Large Scale Covid-19 Detection with Blood Platelets Using Artificial Neural Network

Detection of COVID-19 by using patient’s blood platelets using an artificial neural network to perform large-scale automated testing. The deep learning model uses the CBC test results to detect the virus with an accuracy of 89.26%, which achieve higher accuracy than the widely used RT-PCR test. The model has achieved accuracy with only a thousand rows, which assures that the model can achieve better results with more data. The objective of the paper is to provide an automated and accurate method to detect COVID-19 using machine learning to apply it in large-scale testing, to help in containing the virus and prevent the spread of the virus. The scope of the paper is on the detection methodology using the blood platelets values extracted from the complete blood cycle (CBC) testing and discussing the future possibilities of improving the automated prevention method. The paper uses a dataset provided by Hospital Israelita Albert Einstein in Brazil, which has over 50 features of different tests as Influenza A, Influenza B, Uria, etc.…, many of these features had missing values or wasn’t relevant to the existence of the virus in the body;therefore, the feature selection led to filtering the 17 blood features. The K-Nearest Neighbour (KNN) Imputation imputed the missing values in the 17 filtered values. The analysis & visualization of the blood features concluded that the complete blood cycle test values highly correlated with each other, and there is a change occurs due to the presence of the virus is clear, especially to Eosinophils and Leukocytes, at which they represent the white blood cells forming the immune system that reacts against any virus or bacteria attracting the human body. The paper has applied five different machine learning algorithms: logistic regression, support vector machine (SVM), random forest, K neighbours and decision tree. Meanwhile, in the second approach, used an artificial neural network with intensive use of network optimization and hyperparameter tuning to achieve the highest accuracy possible. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.