Change search
ReferencesLink to record
Permanent link

Direct link
Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2010 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario.

This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others.

Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Place, publisher, year, edition, pages
2010. , 40 p.
Keyword [en]
naïve bayes, decision trees, KNN
National Category
Computer and Information Science
URN: urn:nbn:se:du-20790OAI: diva2:894569
Available from: 2016-01-15 Created: 2016-01-15 Last updated: 2016-01-15Bibliographically approved

Open Access in DiVA

fulltext(962 kB)38 downloads
File information
File name FULLTEXT01.pdfFile size 962 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Computer Engineering
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 38 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

Total: 101 hits
ReferencesLink to record
Permanent link

Direct link