Högskolan i Skövde

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Prediction of the number of weekly covid-19 infections: A comparison of machine learning methods
Högskolan i Skövde, Institutionen för informationsteknologi.
2022 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
Abstract [en]

The thesis two-folded problem aim was to identify and evaluate candidate Machine Learning (ML) methods and performance methods, for predicting the weekly number of covid-19 infections. The two-folded problem aim was created from studying public health studies where several challenges were identified. One challenge identified was the lack of using sophisticated and hybrid ML methods in the public health research area. In this thesis a comparison of ML methods for predicting the number of covid-19 weekly infections has been performed.

A dataset taken from the Public Health Agency in Sweden consisting of 101weeks divided into a 60 % training set and a 40% testing set was used in the evaluation.

Five candidate ML methods have been investigated in this thesis called Support Vector Regressor (SVR), Long Short Term Memory (LSTM), Gated Recurrent Network (GRU), Bidirectional-LSTM (BI-LSTM) and LSTM-Convolutional Neural Network (LSTM-CNN). These methods have been evaluated based on three performance measurements called Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R2. The evaluation of these candidate ML resulted in the LSTM-CNN model performing the best on RMSE, MAE and R2.

Ort, förlag, år, upplaga, sidor
2022. , s. 68
Nyckelord [en]
Machine learning, deep learning, covid-19, public health science, number of infection, regression, long short term memory, gated recurrent unit, support vector regressor, long short term memory-convolutional neural network, bidirectional-long short term memory
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
URN: urn:nbn:se:his:diva-21302OAI: oai:DiVA.org:his-21302DiVA, id: diva2:1672236
Ämne / kurs
Informationsteknologi
Utbildningsprogram
Data Science - masterprogram
Handledare
Examinatorer
Tillgänglig från: 2022-06-19 Skapad: 2022-06-19 Senast uppdaterad: 2022-06-19Bibliografiskt granskad

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Institutionen för informationsteknologi
Systemvetenskap, informationssystem och informatik

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