diplomsko delo
Romana Koprivec (Avtor), Zoran Bosnić (Mentor)

Povzetek

Napovedovanje bolezni ledvic z metodami strojnega učenja

Ključne besede

strojno učenje;regresija;bioinformatika;obstruktivna nefropatija;računalništvo;univerzitetni študij;diplomske naloge;

Podatki

Jezik: Slovenski jezik
Leto izida:
Tipologija: 2.11 - Diplomsko delo
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
Založnik: [R. Koprivec]
UDK: 004.85:616.61(043.2)
COBISS: 9072980 Povezava se bo odprla v novem oknu
Št. ogledov: 43
Št. prenosov: 4
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Angleški jezik
Sekundarni naslov: Prediction of a kidney disease using machine learning methods
Sekundarni povzetek: A group of researchers announced a competition for making diagnostic models that predict intensity of obstructive nephropathy. In this thesis we have developed a regression model which can predict the level of the illness according to the molecular profile. This model is intended to predict two target values: pelvic diameter and differential renal function. We wanted to improve the prediction models with the knowledge about potential connections between biological levels. We have implemented a procedure with the help of the published data about connections between attributes and Mann-Whitney test, which connects two different databases on two different groups of samples. The lowest relative root mean squared error (RRMSE) has been achieved using locally weighted regression. RRMSE of the method has been lower on connected databases than on the original databases. The result on test dataset has improved as well. The best estimated regression methods on train and test dataset have differentiated because of extraordinary small number of samples. Some models had higher RRMSE on train dataset; however, they achieved better results on test dataset. Nevertheless, with the best model according to RRMSE on train dataset we have exceeded the best-published result on the competition for 81%.
Sekundarne ključne besede: machine learning;regression;bioinformatics;obstructive nephropathy;computer science;diploma;
Vrsta datoteke: application/pdf
Vrsta dela (COBISS): Diplomsko delo
Komentar na gradivo: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Strani: 34 str.
ID: 24063149