Data Mining Approach to Classify Tumour Morphology using Naïve Bayes Algorithm
Shahmirul Hafizullah Imanuddin, Irfan Darmawan, Rahmat Fauzi
Shahmirul Hafizullah Imanuddin
Available Online March 2019.
- https://doi.org/10.2991/icoiese-18.2019.51How to use a DOI?
- Naive Bayes Algorithm, Classification, RapidMiner, Tumour.
- Tumours are a very sickly disease and recorded as the second killer disease in the world. This is because until now tumour still not found a drug that can really cure it. In Indonesia itself has many people who have suffered from tumours. Ignorance makes people reluctant to observe the early symptoms of tumour. In addition, the hospital also has problems with what type of tumour is most common in the community and their target of socialization to the community and hospital environment. In this study, the topic of discussion focused on making patterns of tumour disease patients using Naive Bayes algorithm on Rapidminer tools using supporting variables of sex, age and place of tumour in the body. The output of this study is a posterior probability value of each variable with 66.76 percent accuracy. In addition there is also a density value in the form of a chart on each supporting variable.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shahmirul Hafizullah Imanuddin AU - Irfan Darmawan AU - Rahmat Fauzi PY - 2019/03 DA - 2019/03 TI - Data Mining Approach to Classify Tumour Morphology using Naïve Bayes Algorithm BT - 2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018) PB - Atlantis Press SP - 288 EP - 291 SN - 2589-4943 UR - https://doi.org/10.2991/icoiese-18.2019.51 DO - https://doi.org/10.2991/icoiese-18.2019.51 ID - HafizullahImanuddin2019/03 ER -