Year: 2025
Type: article
Abstract: A high-accuracy DNA-based cancer risk predictor was developed by blending Logistic Regression with Gaussian Naive Bayes, and its hyperparameters were optimized via grid search. Five cancer types (BRCA1, KIRC, COAD, LUAD, PRAD) were classified in a cohort of 390 patients. Accuracies of 100% were attained for BRCA1, KIRC, and COAD, while 98% was achieved for LUAD and PRAD—representing improvements of 1–2% over recent deep-learning and multi-omic benchmarks. A micro- and macro-average ROC AUC of 0.99 was obtained. The blended ensemble was shown to outperform each individual algorithm and surpass existing state-of-the-art methods, providing a lightweight, interpretable, and highly effective tool for early cancer prediction.
Source: Scientific Reports
Author: Seyed Matin Malakouti
Institution: Amirkabir University of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4416149172