Prediction of Lung Cancer with Fuzzy Logic Methods: A Systematic Review

Abstract

According to the World Health Organization (WHO), lung cancer is the primary cause of cancer-related deaths worldwide and is known to have the highest mortality rate among both men and women. Early and accurate detection of lung cancer can lead to better treatments and outcomes. Different methods can be used to diagnose a complex and uncertain disease, such as lung cancer, and fuzzy logic is one of these methods. The challenge of diagnosing lung cancer nodules, coupled with the high mortality rate of lung cancer, underscores the significance of using fuzzy logic. Fuzzy logic offers a problem-solving approach that relies on logical rules and if-then statements, incorporating human experience. There are many studies in the literature on the diagnosis of lung cancer with fuzzy logic approaches, and it is important to examine these studies to provide a general framework on this subject. Therefore, this systematic review aims to synthesize and evaluate the current evidence on the application of fuzzy logic methods in lung cancer prediction and diagnosis, and thus can provide a guide to researchers and decision makers who want to work in this field. The study followed the PRISMA guidelines for systematic reviews, ensuring a structured and transparent approach to the research process. Scopus, Web of Science (WoS), PubMed, and IEEE Explore databases were searched to find relevant studies, and appropriate studies were carefully reviewed. The inclusion and exclusion criteria were clearly defined, and the analysis process was performed independently. Out of 222 initially identified studies, 51 met the inclusion criteria and were analyzed in depth. The most commonly used fuzzy logic techniques were Fuzzy Rule-Based Systems, Fuzzy C-Means Clustering, and Fuzzy Inference Systems. Studies reported accuracy rates ranging from 85% to 98% in lung cancer prediction and diagnosis. Hybrid models combining fuzzy logic with other machine learning techniques showed particularly promising results. Fuzzy logic methods demonstrate significant potential in improving the accuracy of lung cancer prediction and diagnosis. However, further research is needed to standardize approaches and validate these methods in large-scale clinical settings. The integration of fuzzy logic with other artificial intelligence techniques presents a promising direction for future developments in lung cancer diagnostics.

Keywords

• Fuzzy logic
• lung cancer
• prediction
• diagnosis
• systematic review

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