Event Title

Supervised and Unsupervised Machine Learning Algorithms: A comparative study

Session

Computer Science and Communication Engineering

Description

Machine Learning (ML) algorithms are used as powerful predictors, which are divided in four main categories based on their usage purpose. Supervised and unsupervised learning are ML paradigms which are focus of this paper. ML is a great possibility for healthcare system that provides analysis of the patients’ datasets in order to detect and predict different diseases. In this paper, comparison between two algorithms has been done in order to reveal advantages and disadvantages based on the reviewed literature. Afterwards both of the algorithms were used to predict the diseases of the patients based on specific purposes and parameters. The comparison in theoretical and practical aspects is represented in order to find the best performance between these two algorithms; a detailed guide which algorithm is the best fit and more effective for particular situations and datasets. Finally the gained results will contribute on prediction of the patient diseases.

Keywords:

machine learning, algorithms, supervised learning, unsupervised learning

Session Chair

Bertan Karahoda

Session Co-Chair

Krenare Pireva

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-550-19-2

Location

Pristina, Kosovo

Start Date

26-10-2019 1:30 PM

End Date

26-10-2019 3:00 PM

DOI

10.33107/ubt-ic.2019.272

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Oct 26th, 1:30 PM Oct 26th, 3:00 PM

Supervised and Unsupervised Machine Learning Algorithms: A comparative study

Pristina, Kosovo

Machine Learning (ML) algorithms are used as powerful predictors, which are divided in four main categories based on their usage purpose. Supervised and unsupervised learning are ML paradigms which are focus of this paper. ML is a great possibility for healthcare system that provides analysis of the patients’ datasets in order to detect and predict different diseases. In this paper, comparison between two algorithms has been done in order to reveal advantages and disadvantages based on the reviewed literature. Afterwards both of the algorithms were used to predict the diseases of the patients based on specific purposes and parameters. The comparison in theoretical and practical aspects is represented in order to find the best performance between these two algorithms; a detailed guide which algorithm is the best fit and more effective for particular situations and datasets. Finally the gained results will contribute on prediction of the patient diseases.