Análisis comparativo de predicción dentro de bases de datos de cáncer: Una aplicación de aprendizaje automático

Translated title of the contribution: Comparative analysis of prediction within cancer databases: A machine learning application

Gabriel Mauricio Martínez-Toro, Dewar Rico-Bautista, Efrén Romero-Riaño

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Cancer is not only a disease, it is a set of diseases with a great impact on public health. In that sense, efforts to consolidate methods of analysis based on large data that contribute to its prediction, is an area of special interest for scientists and data analysts. The objective of this paper is to compare the performance of method prediction: i) Logistic regression, ii) K Nearest Neighbor, iii) K-means, iv) Random Forest, v) Support Vector Machine, vi) Linear Discriminant Analysis, vii) Gaussian Naive Bayes viii) Multilayer Perceptron, within a cancer database. In the case of unsupervised learning models, the relevance of the centroids for the k means algorithm is evident, as well as the learning rate assignments and parameters for the Multilayer Perceptron case. In the case of supervised learning models, SVM performs best.

Translated title of the contributionComparative analysis of prediction within cancer databases: A machine learning application
Original languageSpanish
Pages (from-to)113-122
Number of pages10
JournalRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
Issue numberE17
StatePublished - 1 Jan 2019

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