hkr.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Classification Performance Between Machine Learning and Traditional Programming in Java
Kristianstad University, Faculty of Natural Science.
Kristianstad University, Faculty of Natural Science.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This study proposes a performance comparison between two Java applications with two different programming approaches, machine learning, and traditional programming. A case where both machine learning and traditional programming can be applied is a classification problem with numeric values. The data is heart disease dataset since heart disease is the leading cause of death in the USA. Performance analysis of both applications is carried to state the differences in four main points; the development time for each application, code complexity, and time complexity of the implemented algorithms, the classification accuracy results, and the resource consumption of each application. The machine learning Java application is built with the help of WEKA library and using its NaiveBayes class to build the model and evaluate its accuracy. While the traditional programming Java application is built with the help of a cardiologist as an expert in the field of the problem to identify the injury indications values. The findings of this study are that the traditional programming application scored better performance results in development time, code complexity, and resource consumption. It scored a classification accuracy of 80.2% while the Naive Bayes algorithms in the machine learning application scored an accuracy of 85.51% but on the expense of high resource consumption and execution time.

Place, publisher, year, edition, pages
2019. , p. 54
Keywords [en]
Classification performance, algorithms, Java, benchmarking, machine learning, naive bayes, heart disease, supervised learning, WEKA
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hkr:diva-20009OAI: oai:DiVA.org:hkr-20009DiVA, id: diva2:1355131
Educational program
Bachelor programme in Computer Software Development
Supervisors
Examiners
Available from: 2019-10-07 Created: 2019-09-27 Last updated: 2019-10-07Bibliographically approved

Open Access in DiVA

fulltext(998 kB)3 downloads
File information
File name FULLTEXT01.pdfFile size 998 kBChecksum SHA-512
24fd16f3861259cb57105edb28a0db877af58b08f77907acaa6ec41082add787b962d6d5817589c3173f9c2f0190515756089d926d5eacdf128b82d764a22381
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Alassadi, AbdulrahmanIvanauskas, Tadas
By organisation
Faculty of Natural Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 3 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 50 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf