Forecasting of Breast Cancer and Diabetes Using Ensemble Learning
Machine learning algorithm plays an important role in our life. It is the subset of Artificial intelligence. Recently, everyone tries to use AI or try to invent something related to AI for making life easier. In the medical field, Machine learning is used for the recognition and classification of diseases. It can classify cancer, diabetes or other diseases more accurately from datasets. So, we propose a model which is the combination of Support vector machine and Ad boost. This combine method is known as Ensemble learner. In this paper, we are predicting diabetes and breast cancer. We have used SVM for classification purpose then have applied Ad boost for boosting purposes. The number of a diabetes patient is increasing very rapidly. It causes many other diseases like kidney failure; Eye disorder etc. No medicines are invented to prevent diabetes fully. Breast cancer is increasing very rapidly between women. The cost of breast cancer treatment is very high. More researches are running on diabetes and breast cancer. We proposed our model to predict the diseases more accurately rather than the previous models.
Savan Patel, Chapter 2 : SVM (Support Vector Machine) — Theory https://medium.com/machine-learning-101/chapter-2-svm-supportvector-machine-theory-f0812effc72, Accessed (2017)
Emily Coberly, MedlinePlus-based health information prescriptions: a comparison of email vs paper delivery, J. Innov. Health Infor. (2013) 197-205
Marilyn Fenichel, American Cancer Society Changes Breast Cancer Screening Guidelines to Reflect Analysis of Benefits and Harms, J. Nat. Can. Inst. 108(2016)
Wenqian Chen, Shuyu Chen, Hancui Zhang and Tianshu Wu, A Hybrid Prediction Model for Type 2 Diabetes Using K-means and Decision Tree, 8th IEEE Inter. Confe. Soft. Eng. Serv. Sci., (2017) 386-390.
Dr. Prof. Neeraj, Sakshi Sharma, Renuka Purohit, and Pramod Singh Rathore, Prediction of Recurrence Cancer using J48 Algorithm, 2nd Inter. Conf. Comm.Elect. Syst., (2017) 386-390.
Deepika Verma and Dr. Nidhi Mishra, Analysis and Prediction of Breast Cancer and Diabetes disease datasets using Data mining classification Techniques, Inter. Conf. Intell. Sust. Sys. (2017) 533-538.
Roxana Mirshahvalad and Nastaran Asadi Zanjani, Diabetes Prediction Using Ensemble Perceptron Algorithm, 9th Inter. Confe. Comput. Intell. Comm. Net. (2017)190-194.
Roxana Mirshahvalad and Nastaran Asadi Zanjani, Diabetes Prediction Using Ensemble Perceptron Algorithm, 9th Inter. Confe. Comput. Intell. Comm. Net. (2017) 190-194.
Janez Demšar and Blaž Zupan, Orange: Data Mining Fruitful and Fun - A Historical Perspective, Informatica, 37(2013) 55-60
Md. Maniruzzaman, Md. Jahanur Rahman, Accurate Diabetes Risk Stratification Using Machine Learning, J. Med. Sys., 42 (2018) 92
Tuba kiyan, Tulay Yildirim, Breast Cancer Diagnosis ¨ Using Statistical Neural Networks, J. Electrical Electron. Eng., 4 (2004) 1149-1153.
W.H. Wolberg, O. L. Mangasarian, Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology, Proc. Natl. Acad. Sci. U.S.A., 87 (1990) 9193–9196.
Sander Greenland, Stephen J. Senn, J Kenneth,J Rothman , John B. Carlin, Charles Poole, Goodman, and Douglas G. Altman, Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations, Europ. J. Epid. 31 (2016) 337–350.
Copyright (c) 2019 Sri Shakthi Institute of Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.