The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like Computational linguistics, image identification, and autonomous systems. With the increasing demand for intelligent systems, it has become crucial to comprehend the different categories of machine acquiring knowledge systems along with their applications in the present world. This paper presents actual use cases of machine learning, including cancer classification, and how machine learning algorithms have been implemented on medical data to categorize diverse forms of cancer and anticipate their outcomes. The paper also discusses supervised, unsupervised, and reinforcement learning, highlighting the benefits and disadvantages of each category of Computational intelligence system. The conclusions of this systematic study on machine learning methods and applications in cancer classification have numerous implications. The main lesson is that through accurate classification of cancer kinds, patient outcome prediction, and identification of possible therapeutic targets, machine learning holds enormous potential for improving cancer diagnosis and therapy. This review offers readers with a broad understanding as of the present advancements in machine learning applied to cancer classification today, empowering them to decide for themselves whether to use these methods in clinical settings. Lastly, the paper wraps up by engaging in a discussion on the future of machine learning, including the potential for new types of systems to be developed as the field advances. Overall, the information included in this survey article is useful for scholars, practitioners, and individuals interested in gaining knowledge about the fundamentals of machine learning and its various applications in different areas of activities.

In the era of healthcare and its related research fields, the dimensionality problem of high-dimensional data is a massive challenge as it is crucial to identify significant genes while conducting research on diseases like cancer. As a result, studying new Machine Learning (ML) techniques for raw gene expression biomedical data is an important field of research. Disease detection, sample classification, and early disease prediction are all important analyses of high-dimensional biomedical data in the field of bioinformatics. Recently, machine-learning techniques have dramatically improved the analysis of high-dimension biomedical data sets. Nonetheless, researchers’ studies on biomedical data faced the challenge of vast dimensions, i.e., the vast features (genes) with a very low sample space. In this paper, two-dimensionality reduction methods, feature selection, and feature extraction are introduced with a systematic comparison of several dimension reduction techniques for the analysis of high-dimensional gene expression biomedical data. We presented a systematic review of some of the most popular nature-inspired algorithms and analyzed them. The paper is mainly focused on the original principles behind each of the algorithms and their applications for cancer classification and prediction from gene expression data. Lastly, the advantages and disadvantages of nature-inspired algorithms for biomedical data are evaluated. This review paper may guide researchers to choose the most effective algorithm for cancer classification and prediction for the satisfactory analysis of high-dimensional biomedical data.

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.

Problem Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process. Aim This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity. Methods We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India. Results The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls. Conclusion Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.