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Breast cancer affects the majority of women worldwide, and it is the second most common cause of death among women. However, if cancer is detected early and treated properly, it is possible to be cured of the condition. Early detection of breast cancer can dramatically improve the prognosis and chances of survival by allowing patients to receive timely clinical therapy. Furthermore, precise benign tumour classification can help patients avoid unneeded treatment. This paper study uses Convolution Neural Networks for Image dataset and K-Nearest Neighbour (KNN), Decision Tree (CART), Support Vector Machine (SVM), and Naïve Bayes for numerical dataset, whose features are obtained from digitised image of breast mass, as to forecast and analyse cancer databases in order to improve accuracy. The dataset will be analysed, evaluated, and model is trained as part of the process. Finally, both image and numerical test data will be used for prediction.

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for isolated words. The efficiency of ensembled classification model is explored.