Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used a dataset obtained from the National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep learning methods were included in the accuracy assessment. Classical decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector machine (SVM), and ensemble techniques (ET) algorithms were used. Probabilistic neural network (PNN), deep neural network (DNN), and recurrent neural network (RNN) methods were used for comparison. Feature selection and its effect on accuracy were also investigated. The results showed that decision trees and ensemble techniques outperformed the other techniques, as they both achieved a 98.7% accuracy.

Since the earliest beginning of history, architectural fortresses have acquired varied forms and masses. It was possible to interpret justifications for using many of them; however, usage of some of these forms and masses is still considered as a perplexing mystery for scholars, analysts and interested persons in the topic of mass formulation in architecture. For example, the Giza Pyramids in Egypt which were built 4500 years ago, whose pyramidal mass, which was the output of several astronomic and mathematical relations in the pyramid form, the world is still till our present day analyzing the inherent ideas beyond them. Throughout ages, ideas of approaching architectural masses differed till the architectural masses of buildings at the end of the twentieth and the onset of the twenty-first centuries reflected several matters. The most important among these are the architectural, constructive and mechanical sciences and the high technological reality. Thus, the architectural form has turned from static masses to dynamic masses during the digital and information revolution which concentrated on producing supplementary ideas and programs in design. In fact, design by the help of computer programs achieves the uniqueness and distinctiveness of ideas, and the inventiveness of the mass and its transformations so that ideas of the architect are freed away from the traditional limitations and restrictions. With the development of computer technology with the digital, then the information, revolution, numerous generations appeared : the first was the computer technology (computer programs), the second was the digital (internet) and the third was the information (Infomedia). This development has had its direct effect on architecture and urbanization. Architecture has had to correspond with modern needs for users; therefore, what was called digital architectural thinking or the third generation for creating architectural masses, and in turn the third generation of the contemporary urbanization texture, has emerged. This represents the research problem in what we do not find in our local architecture due to following the world in this technology because architects cannot identify it. Hence, it appears the importance of getting to identify what the architectural thought tools have reached and its passage through several generations so that it keeps updated with the age of technology and employs what technology provides of contribution in creating a building or a city that undergoes all kinds of tests before its implementation. All these ideas are interwoven is an architectural entity that contains them which represents a digital reflection on the urban design controlled by information by what is called urbanization and architecture of the third generation of the age of technology in the twentieth-first century. Hence, the research is divided into several axes. To begin with, the first axis deals with the