Journal of Science, Technology and Environment Informatics |
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RESEARCH ARTICLE:
Analysis of classification algorithms for liver disease diagnosis
Shapla Rani Ghosh and Sajjad Waheed
Dept. of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh.
Received: 20.03.17, Revised: 05.06.17, Date of Publication: 25 June 2017.
J. Sci. Technol. Environ. Inform. | Volume 05, Issue 01, pp. 361-370
Crossref: https://doi.org/10.18801/jstei.050117.38
Analysis of classification algorithms for liver disease diagnosis
Shapla Rani Ghosh and Sajjad Waheed
Dept. of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh.
Received: 20.03.17, Revised: 05.06.17, Date of Publication: 25 June 2017.
J. Sci. Technol. Environ. Inform. | Volume 05, Issue 01, pp. 361-370
Crossref: https://doi.org/10.18801/jstei.050117.38
FULL TEXT PDF:
| 38_jstei_analysis_of_classification_algorithms_for_liver_disease_diagnosis.pdf |
Title: Analysis of classification algorithms for liver disease diagnosis
Abstract: Now a days liver disease is extending markedly due to excessive alcohol consumption, smoking, drinking arsenic contaminated water, obesity, low immunity and by inheritance. Liver cancer symptoms may include jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, general itching. Selective algorithms may be used on medical instruments (e.g. CT scanner, MRI, Ultra sono, ECG etc.) to lessen time and cost on hepatic disease diagnosis. Here some of the algorithms such as, Naive Bayes classification (NBC), Bagging, KStar, Logistic and REP tree were used to evaluate the accuracy, precision, sensitivity and specificity. For these two data sets of UCLA and AP were considered to find out the best algorithm. The whole analysis was done using the software Weka 3.6.10. It was revealed that, KStar algorithm had the maximum accuracy, precision, sensitivity and specificity. On the other, minimum accuracy was obtained from NBC. Therefore K* algorithm can be used on diagnosis tools or instruments for rapid identification of specific liver disorder.
Key Words: Classification Algorithms (Bagging, K-star, NBC, Logistic, Rep Tree), Liver disease diagnosis
Abstract: Now a days liver disease is extending markedly due to excessive alcohol consumption, smoking, drinking arsenic contaminated water, obesity, low immunity and by inheritance. Liver cancer symptoms may include jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, general itching. Selective algorithms may be used on medical instruments (e.g. CT scanner, MRI, Ultra sono, ECG etc.) to lessen time and cost on hepatic disease diagnosis. Here some of the algorithms such as, Naive Bayes classification (NBC), Bagging, KStar, Logistic and REP tree were used to evaluate the accuracy, precision, sensitivity and specificity. For these two data sets of UCLA and AP were considered to find out the best algorithm. The whole analysis was done using the software Weka 3.6.10. It was revealed that, KStar algorithm had the maximum accuracy, precision, sensitivity and specificity. On the other, minimum accuracy was obtained from NBC. Therefore K* algorithm can be used on diagnosis tools or instruments for rapid identification of specific liver disorder.
Key Words: Classification Algorithms (Bagging, K-star, NBC, Logistic, Rep Tree), Liver disease diagnosis
HOW TO CITE THIS ARTICLE
APA (American Psychological Association)
Ghosh, S. R. and Waheed, S. (2017). Analysis of classification algorithms for liver disease diagnosis. Journal of Science, Technology and Environment Informatics, 05(01), 361-3270.
MLA (Modern Language Association)
Ghosh, S. R. and Waheed, S. “Analysis of classification algorithms for liver disease diagnosis”. Journal of Science, Technology and Environment Informatics, 05.01 (2017): 361-370.
Chicago and or Turabian
Ghosh, S. R. and Waheed, S. “Analysis of classification algorithms for liver disease diagnosis”. Journal of Science, Technology and Environment Informatics, 05, no. 01 (2017): 361-370.
APA (American Psychological Association)
Ghosh, S. R. and Waheed, S. (2017). Analysis of classification algorithms for liver disease diagnosis. Journal of Science, Technology and Environment Informatics, 05(01), 361-3270.
MLA (Modern Language Association)
Ghosh, S. R. and Waheed, S. “Analysis of classification algorithms for liver disease diagnosis”. Journal of Science, Technology and Environment Informatics, 05.01 (2017): 361-370.
Chicago and or Turabian
Ghosh, S. R. and Waheed, S. “Analysis of classification algorithms for liver disease diagnosis”. Journal of Science, Technology and Environment Informatics, 05, no. 01 (2017): 361-370.
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