Abstract
In this paper four machine learning algorithms are compared in order to predict if a cell nucleus is benign or malignant using the Breast Cancer Wisconsin (Diagnostic) Data Set. The algorithms are K-Nearest Neighbours, Classification and Regression Trees (CART), Naïve Bayes and Support Vector Machines with Radial Basis Function Kernel. Data visualization and Pre- Processing using PCA will help in the understanding and the preparation of the dataset for the training phase while parameter tuning will determine the optimal parameter for every model using R as programming language. Also, 10-fold Cross Validation is used as a resampling method after comparing it with Bootstrapping, as it is the most efficient out of the two. In the end, our comparison shows that the machine learning model that marked the highest Accuracy is the one that is trained using K Nearest Neighbours. Nowadays, one of the most common forms of cancer among women is breast cancer with more than one million cases and nearly 600,000 deaths occurring worldwide annually [1]. It is the second leading cause of death among women and thus it must be detected at an early stage in order not to become fatal [2]. Thus, the importance of diagnosing if a biopsied cell is benign or malignant is vital. However, this process is quite complicated as it involves several stages of gathering and analysing samples with many variables, making the final diagnosis a demanding and timely procedure. The rapid growth of Artificial Intelligence and Machine learning and their implementation in Medicine give us a new perspective in the way we process and analyse medical data. Medical experts can use Data Mining techniques and improve their decision making by extracting useful information from massive amounts of data.
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Kaklamanis, M.M., Filippakis, M.Ε., Touloupos, M., Christodoulou, K. (2020). An Experimental Comparison of Machine Learning Classification Algorithms for Breast Cancer Diagnosis. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2019. Lecture Notes in Business Information Processing, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-030-44322-1_2
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