International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

Author(s) Shreyas Hegde, Vinay Kumar
Country India
Abstract This paper proposes a deep learning method to predict cancer risk from gene symbols using a multilayer perceptron (MLP) feed forward neural network. The paper uses a data set of gene symbols and their corresponding cancer risk labels, obtained from a DNA microarray analysis. The paper then builds and
compares different machine learning models, such as logistic regression, linear discriminant analysis, quadratic discriminant analysis, decision tree classifier, gaussian nb, ada boost classifier, gaussian process classifier, support vector machine, and random forest. Deep learning MLP model is built, tuned and optimized for hyperparameters which improves the accuracy significantly by 9.09% compared to the best machine learning model. The paper evaluates the performance of the MLP model on the data set using accuracy, precision, recall, and F1-score metrics. This paper contributes to the field of machine learning and bioinformatics by providing a novel and effective way to predict cancer risk from gene symbols.
Keywords Multilayer Perceptron, Feed Forward Neural Network, Gene Symbols, Differential Analysis
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-17
Cite This Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach - Shreyas Hegde, Vinay Kumar - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13568
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.13568
Short DOI https://doi.org/gtjtzn
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E-ISSN 2582-2160
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