An experimental comparison of machine learning classification algorithms for breast cancer diagnosis

Markos Marios Kaklamanis, Michael Filippakis, Marios Touloupos, Klitos Christodoulou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationInformation Systems - 16th European, Mediterranean, and Middle Eastern Conference, EMCIS 2019, Proceedings
EditorsMarinos Themistocleous, Maria Papadaki
PublisherSpringer India
Pages18-30
Number of pages13
ISBN (Print)9783030443214
DOIs
Publication statusPublished - 1 Jan 2020
Event16th European, Mediterranean, and Middle Eastern Conference on Information System, EMCIS 2019 - Dubai, United Arab Emirates
Duration: 9 Dec 201910 Dec 2019

Publication series

NameLecture Notes in Business Information Processing
Volume381 LNBIP
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Conference

Conference16th European, Mediterranean, and Middle Eastern Conference on Information System, EMCIS 2019
CountryUnited Arab Emirates
CityDubai
Period9/12/1910/12/19

Keywords

  • Breast cancer
  • Classification
  • Data mining
  • Diagnosis
  • Machine learning

Fingerprint Dive into the research topics of 'An experimental comparison of machine learning classification algorithms for breast cancer diagnosis'. Together they form a unique fingerprint.

  • Cite this

    Kaklamanis, M. M., Filippakis, M., Touloupos, M., & Christodoulou, K. (2020). An experimental comparison of machine learning classification algorithms for breast cancer diagnosis. In M. Themistocleous, & M. Papadaki (Eds.), Information Systems - 16th European, Mediterranean, and Middle Eastern Conference, EMCIS 2019, Proceedings (pp. 18-30). (Lecture Notes in Business Information Processing; Vol. 381 LNBIP). Springer India. https://doi.org/10.1007/978-3-030-44322-1_2