Research on lung cancer diagnosis based on machine learning

Abstract

Background

In clinical diagnosis, determining the level of malignancy in tumors and differentiating between benign and malignant tumors are common classification challenges. Accurate and early diagnosis is essential for targeted treatment, and machine learning methods can assist in making these judgments.

Methods

This paper focuses on the classification of the lung tissue as benign or malignant and assessing the degree of aggressiveness in lung cancer. The study employed artificial neural network (ANN), logistic regression, and ridge penalized logistic regression, which are methods without built-in feature selection. Additionally, lasso penalized logistic regression, elastic-net penalized logistic regression, and sparse logistic regression with the hybrid L1/2 + 2 regularization (HLR), which are methods with built-in feature selection, were also utilized.

Results

In the study on classifying benign and malignant lung tissue, ANN demonstrated the best predictive performance among the methods without built-in feature selection, achieving an average test accuracy of 91.82%. Among the methods with built-in feature selection, HLR outperformed the others with an average test accuracy of 96.67%. When determining the level of malignancy in lung tumors, ANN surpassed other methods without built-in feature selection, attaining an average test accuracy of 84.74%. In comparison, HLR exceeded the performance of other methods with built-in feature selection, reaching an average test accuracy of 93.33%.

Conclusions

The experimental results indicated that HLR with built-in feature selection and ANN without built-in feature selection exhibited strong competitiveness among the methods investigated in both classifying benign and malignant lung tissue and assessing the degree of aggressiveness in lung cancer.