Usporedba metoda za klasifikaciju podataka temeljem skupa podataka koji opisuje simptome i faktore za srčana oboljenja

undergraduate thesis

• Title (english): A comparison of data classification methods based on a dataset describing symptoms and factors for heart disease
• Author: Matej Gašparić
• Mentor: Tomislav Stipančić (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2024-02-28, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering

Abstract

U svijetu podataka i velike količine informacija, algoritmi umjetne inteligencije postaju sve
značajniji. U ovisnosti o vrsti i strukturi podataka, metode strojnog učenja koriste se prilikom
predviđanja, odlučivanja, klasifikacije, prepoznavanja i slično. U ovom radu koristit će se
nekoliko metoda za klasifikaciju koje će biti primijenjene na skupu podataka koji opisuje
simptome i faktore značajne kod pojave srčanih oboljenja. Korištene metode su: logistička
regresija, stablo odlučivanja, nasumična šuma, metoda potpornih vektora, k-najbližih susjeda
od kojih je svaka ukratko objašnjena prije obrade podataka i usporedbe rezultata. Prikazana je
priprema podataka i njihova vizualizacija te na kraju implementacija algoritama i usporedba
njihovih rezultata prema mjerama vrednovanja modela strojnog učenja.

Abstract (english)

In the world of data and a vast amount of information, artificial intelligence algorithms are
becoming increasingly significant. Depending on the type and structure of data, machine
learning methods are used for prediction, decision-making, classification, recognition, and
similar tasks. In this paper, several classification methods will be utilized and applied to a
dataset describing symptoms and factors relevant to the occurrence of heart diseases. The
methods employed include logistic regression, decision tree, random forest, support vector
machine, and k-nearest neighbors, each briefly explained before data processing and result
comparison. Data preparation and visualization are presented, followed by the implementation
of algorithms and a comparison of their results based on machine learning model evaluation
metrics.

• Keywords: algoritam; strojno učenje; logistička regresija; stablo odlučivanja; nasumična šuma; metoda potpornih vektora; k-najbližih susjeda; umjetna inteligencija; srčane bolesti
• Keywords (english): algorithm; machine learning; logistic regression; decision tree; random forest; support vector machine; k-nearest neighbors; artificial intelligence; heart diseases
• Language: croatian
• URN:NBN: urn:nbn:hr:235:578793
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2024-02-24 14:02:53