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An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data.

Hanaa Fathi¹, Hussain AlSalman², Abdu Gumaei³

¹Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Al Minufya, Egypt.

Computational Intelligence and Neuroscience

|January 10, 2022

Summary

This study introduces a hybrid machine learning model for cancer classification using gene expression data. The approach effectively reduces genes, identifies key features, and improves diagnostic accuracy.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cancer remains a leading cause of death globally.
Microarray gene expression profiling is crucial for cancer diagnosis, prognosis, and treatment.
High-dimensional, redundant gene expression data presents significant classification challenges.

Purpose of the Study:

To develop a hybrid machine learning approach for effective cancer classification.
To address the NP-Hard problem of gene expression profile classification.
To reduce dimensionality and select informative genes from microarray data.

Main Methods:

Utilized a hybrid model combining Pearson's correlation coefficient for feature selection/reduction.
Employed a Decision Tree classifier for its interpretability and ease of use.
Optimized the Decision Tree's maximum depth hyperparameter using Grid Search Cross-Validation (CV).

Main Results:

Evaluated the model on seven standard microarray cancer datasets.
Demonstrated significant reduction in the number of genes required for classification.
Showcased enhanced classification accuracy, specificity, sensitivity, F1-score, and AUC.

Conclusions:

The proposed hybrid strategy effectively reduces gene numbers and selects the most informative features.
The model significantly improves cancer classification accuracy using gene expression data.
This approach offers a robust method for analyzing complex genomic datasets in oncology.