Analiza metagenomskih podataka u svrhu predviđanja dijabetesa

undergraduate thesis

• Title (english): Diabetes prediction using metagenomic datasets
• Author: Bruna Kaštela
• Mentor: Mirjana Domazet-Lošo (mentor)
• Committee member: Mirjana Domazet-Lošo (predsjednik povjerenstva)
• Committee member: Mario Brčić (član povjerenstva)
• Committee member: Mihael Kovač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2023-06-30, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Strojno učenje sve je popularniji alat korišten u medicinskoj dijagnostici. U ovom je radu istraženo strojno učenje u svrhu predikcije šećerne bolesti na temelju metagenomskih podataka pacijenata. Modeli su izgrađeni algoritmima slučajne šume, stroja potpornih vektora i ekstremnog pojačivanja gradijentom. Uz to, skupu podataka smanjena je dimenzionalost koristeći različite metode odabira značajki. Kako bi se model bolje prilagodio problemu, napravljena je i verzija algoritma rekurzivne eliminacije za odabir značajki. Dobiveni rezultati su analizirani i vizualizirani te uspoređeni s postojećim rješenjem. Rješenje je predloženo u programskom jeziku Python pomoću biblioteka Scikit-learn i XGBoost.

Abstract (english)

Machine learning is becoming an increasingly popular tool used in medical diagnostics. This study explores machine learning for the purpose of predicting diabetes based on patients’ metagenomic data. Models were built using algorithms such as random forest, support vector machines and extreme gradient boosting. Additionally, the dimensionality of the dataset was reduced using various feature selection methods. To enhance the model’s adaptability to the problem, a custom version of the recursive feature elimination algorithm was additionally implemented. The obtained results were analyzed, visualized and compared to the existing solution. The solution was implemented in the Python programming language using the Scikit-learn and XGBoost libraries.

• Keywords: strojno učenje; odabir značajki; predviđanje dijabetesa; slučajna šuma; SVM; XGBoost
• Keywords (english): machine learning; feature selection; diabetes prediction; random forest; SVM; XGBoost
• Language: croatian
• URN:NBN: urn:nbn:hr:168:211776
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-07-13 10:11:22