MACHINE LEARNING APPROACHES EMPOWERED LUNGS CANCER PREDICTION: A COMPREHENSIVE ANALYSIS

Abstract

Lung cancer is among the most common and vicious tumors across the world, and it is responsible for a large share of cancer-related deaths. When diagnosed early, the chances of treatment success and survival are vastly improved. In this spectrum, machine learning (ML) has proven to be a disruptive approach in medical diagnostics with sophisticated prospects to analyze large-scale multi-dimensional data sets and identify hidden patterns, which are beyond the reach of traditional statistical methods. In this review, we present an exhaustive discussion of modern ML techniques applied to lung cancer prediction, which include classical paradigms (logistic models, decision trees, and support vector machines) and advanced ones (random forests, gradient boosting frameworks, and deep learning networks).

The review will evaluate these models on several grounds such as predictive performance, interpretability, computational cost, and the usefulness of the models in clinical practice. Detailed discussion of key preprocessing techniques, for example, missing data treatment, one-hot encoding categorical variables, tuning feature selection, and class imbalance handling using resampling techniques like SMOTE is discussed in detail. Moreover, the publicly accessible datasets are presented, such as those of clinical charts, genetic information, and imaging-based databases, to illustrate the use of the data in the achievement of accurate and generalizable models.

These purposes are captured in the proceedings of this review in reviewing the models, taking into account aspects predictive performance, interpretability, computational cost, and usability in practice for clinicians. Key preprocessing tasks include Handle Missing, One-Hot Encoding for Categorical Features, Select Features from Tuning, Class Imbalance-Creation of Synthetic Samples-Resampling - SMOTE. An overview of publicly available datasets such as clinical charts, genetic data, and imaging-based databases is given to demonstrate their utility in building precise and generalizable models. The challenges that persist include limited access to datasets, variability of features, possible inherent bias, and practical limitations in deploying ML systems to real-life healthcare environments. The discussion emphasizes the rising demand for XAI in ensuring transparency, trust, and ethical implementation of predictive models. The paper closes with future research directions, addressing hybrid modeling approaches, multi-modal data fusion, and verification of ethical and regulatory compliance for safe and efficacious use of ML in lung-cancer prediction.