New heuristic method for cancer classification using gene expression data

The aim of this research is to develop and evaluate a novel feature selection method based on a local search algorithm (LSFS) in combination with a genetic algorithm (GA) for cancer classification using high-dimensional gene expression data.

The study spanned a period between 2022 and 2024, using data from two medical research centers (Sofia, Bulgaria, and Moscow, Russia). More than 30,000 patient records were analyzed using six open datasets as source data. These datasets included information on blood, breast, colon, and other types of cancer from the GEO and TCGA databases. To prepare the data for analysis, the study employed techniques such as normalization, outlier elimination, and logarithmic transformation. Modern machine learning algorithms, such as random forests, support vector machines, and neural networks, were utilized to assess the effectiveness of the proposed method.

The LSFS + GA method demonstrated the ability to maximize accuracy, sensitivity, and specificity, as well as other classification metrics. Specifically, classification accuracy improved by 8-15% depending on the dataset, sensitivity by 10-20%, and specificity by 5-10%. Additionally, there was a 12-18% reduction in noise in the data. The effectiveness of the model was demonstrated by a reduction in entropy, indicating an increase in classification confidence of 6-9%. Statistical analysis, including cross-validation and ANOVA, confirmed the statistical significance of the improvements achieved with the new method (p < 0.05).

The developed algorithm enables a substantial reduction in computational complexity while maintaining, or even improving, classification accuracy. This is important for analyzing cancer data, as it helps to ensure more reliable results. The study's findings demonstrate the potential of combining local search methods with genetic algorithms to address high-dimensional classification problems in bioinformatics.