Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence

Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others

Estimation of dam reservoir volume fluctuations using artificial neural network and support vector regression

Estimation of reservoir volume fluctuation is important for the operation of dam reservoir, design of hydraulic structures; determine pollution in reservoir and the safety of dams. Artificial Neural Networks (ANN) and support vector regression (SVR) approach provides a common basis for quantitative modeling in this respect. In this study, reservoir volume was estimated using average monthly precipitation, monthly total volume of evaporation, dam spillway discharge volume, released irrigation water amount and periodicity. The data were collected on a monthly basis over the 29 years for Tahtaköprü Dam in the southeast Mediterranean region of Turkey. For this purpose, three well known methods, artificial neural networks, support vector and multiple linear regressions were employed for estimating the reservoir volume. In this paper, a multi layer perception (MLP) methodology is used as the ANN approach. Levenberg-Marquardt training algorithm is used for optimization of the network. MLP and SVR results are compared to multi-linear regression (MLP) model results. The results show that reservoir volume was successfully estimated using ANN and SVR with low mean square error and high correlation coefficients