The week's pick
Random Articles
Reseach Article
A Data Mining Approach for the Diagnosis of Diabetes Mellitus using Random Forest Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 120 - Number 8 |
| Year of Publication: 2015 |
| Authors: Mani Butwall, Shraddha Kumar |
10.5120/21249-4065
|
Mani Butwall, Shraddha Kumar . A Data Mining Approach for the Diagnosis of Diabetes Mellitus using Random Forest Classifier. International Journal of Computer Applications. 120, 8 ( June 2015), 36-39. DOI=10.5120/21249-4065
Abstract
Diabetes mellitus is an interminable disease that forces excessively high human, social and financial expenses for a nation. Additionally, minimizing its commonness rate and in addition its excessive and risky confusions requires viable administration. Diabetes administration depends on close participation between the patient and health awareness experts. Data mining gives a diversity of methods to investigate large data keeping in mind the end goal to find hidden knowledge. This study is an effort to plan and execute a descriptive data mining approach and to devise association standards to envisage diabetes behaviour in arrangement with particular life style parameters, including physical activity and emotional states, especially in elderly diabetics. Proposed methodology is based on Random Forest Classifier.
References
- IDF Diabetes Atlas, 6th edition, "International Diabetes Federation", 2013, Online available at: http://www. idf. org/diabetesatlas
- "Definition, classification and diagnosis of diabetes, Prediabetes and metabolic syndrome", Canadian Journal of Diabetes, Vol. 37, Canadian Diabetes Association. 2013. Online available at: www. canadianjournalofdiabetes. com.
- "Diagnosis and classification of diabetes mellitus", American Diabetes Association, Diabetes Care, vol. 35, Supplement 1, 2012.
- Jack W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. S. Johannes, "Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus", IEEE Symposium on Computer Applications and Medical Care, pp. 261-265, 1988.
- Brieman L, Random Forests Statistics, Department University of California Berkeley, 45, 5-32, 2001.
- Sonu Kumari, Archana Singh, "A Data Mining Approach for the Diagnosis of Diabetes Mellitus", IEEE 7th International Conference on Intelligent Systems and Control (ISCO), pp 373 – 375, 2013.
- Huang, Zhexue, and Michael K. Ng. "A fuzzy k-modes algorithm for clustering categorical data. " Fuzzy Systems, IEEE Transactions on 7. 4 (1999): 446-452.
- Huang, Zhexue. "A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. " DMKD. 1997.
- Kumari, Sonu, and Archana Singh "A data mining approach for the diagnosis of diabetes mellitus" Intelligent Systems and Control (ISCO), 2013 7th International Conference on IEEE, 2013.
- Rajeeb Dey and Vaibhav Bajpai and Gagan Gandhi and Barnali Dey, "Application of artificial neural network technique for diagnosing diabetes mellitus", 2008 IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10
- Breault, Joseph L. , Colin R. Goodall, and Peter J. Fos. "Data mining a diabetic data warehouse" Artificial Intelligence in Medicine 26. 1 (2002): 37-54.
- Sa-ngasoongsong, Akkarapol, and Jongsawas Chongwatpol "An Analysis of Diabetes Risk Factors Using Data Mining Approach" Oklahoma state university, USA (2012)
- Rajesh, K. , and V. Sangeetha. "Application of Data Mining Methods and Techniques for Diabetes Diagnosis. " age 2. 3 (2012)
Index Terms
Keywords