Lung cancer is a prevalent and lethal malignancy characterised by the uncontrolled growth of abnormal cells in lung tissues, often leading to functional impairment and metastasis. With approximately 2 million new cases and 1.8 million deaths annually, it is a significant contributor to cancer mortality, and projections suggest a substantial increase, with an estimated 3.8 million new cases by 2050 and 3.2 million deaths. So, early diagnosis and rapid drug development strategies are required, while genomics is used to enhance its sequential pattern for better, precise, and personalised medicine. Today, Machine Learning (ML) is transforming modern genomics and drug designing by analysing large genomics datasets to identify genomic sequential patterns and uncover mutations for targeted and better treatments and expedites drug discovery by modelling compound interactions with identified biological targets. The PubMed search was performed to identify relevant publications of the last 10 years and reviewed them critically. ML algorithms like Random Forests, Gradient Boosting, Deep Belief Networks, Autoencoders, Support Vector Machines, Convolutional Neural Networks, and Recurrent Neural Networks are extensively used in modern genomics, while Reinforcement Learning, DNN, GANs and GNNs are used for optimised and personalised drug discovery. ML algorithms face data scarcity and interpretability issues, challenging accuracy and integration with experimental validation. Lung cancer therapeutics are experiencing rapid advancements with the integration of ML, showcasing their potential to solve cancer-related problems with remarkable accuracy, often exceeding 95% in specific applications. However, more optimisation is needed to integrate Artificial Intelligence (AI) efficiently to deal with data heterogeneity and for clinical validation.

Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages. Methods: The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting, and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression. Results: The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top five upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top five downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A, and PKP1. The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

Healthcare 5.0 promises a significant shift in individualised healthcare services for assisted living, propelled by developments such as the IoT, big data analytics, AI, and electronic health systems. This chapter analyses how these technologies affect care delivery in assisted living facilities. IoT technology, such as cognitive sensors, allows continuous monitoring, boosting safety and ensuring prompt responses in crucial situations. AI and machine learning enhance personalised care by employing predictive analytics to anticipate health issues and adjust medication regimens, including medication, nutrition, and exercise. Telemedicine facilitates remote consultations, which improves healthcare accessibility and allows for fast changes to treatment plans. Big data analytics integrate vast amounts of health information to enable personalised medicine by designing therapies based on individual genetic profiles and health histories. Although these innovations significantly enhance healthcare, it is critical to address issues like data quality, privacy concerns, decreased human participation, and the cost and accessibility of these technologies to realise their promise fully. This chapter provides a detailed evaluation of the current and future implications of healthcare 5.0 technology for assisted living. It offers healthcare practitioners, technologists, lawmakers, and patients valuable insights. <!----> <!----> <!----> <!---->

Lung cancer remains a leading cause of cancer-related mortality worldwide, with early and accurate diagnosis posing a critical challenge for improving patient outcomes. Gene expression data provide crucial insights for lung cancer classification by revealing underlying biological mechanisms. However, the high dimensionality of such data presents challenges, including computational complexity and overfitting risks. This study proposes a hybrid feature extraction framework combining Principal Component Analysis (PCA) and Mutual Information (MI) to address these issues. PCA reduces dimensionality by capturing key variance patterns, while MI selects features highly relevant to the target class, ensuring an informative and concise feature set. Gene expression datasets from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) were integrated, focusing on common genes. The hybrid PCA-MI framework was applied to rank genes, and the selected features were used to train a Convolutional Neural Network (CNN) for lung cancer classification. The genes ranked by the hybrid model were further analysed using protein-protein interaction (PPI) networks to identify hub genes, enhancing biological interpretability. The proposed framework was benchmarked against ten other feature extraction methods, including Lasso, Random Forest, Autoencoder, and PCA alone. The CNN classifier achieved superior performance with the PCA-MI features, attaining 98% accuracy and 98% precision. Training and validation curves demonstrated stable learning behaviour, and confusion matrix analysis confirmed robust predictions. Hub gene identification through PPI analysis validated the biological significance of the ranked genes. This study presents a robust framework for lung cancer classification by leveraging the strengths of PCA and MI, integrating deep learning and PPI analysis to address high-dimensional data challenges, and setting a foundation for future research in multi-omics data integration and enhanced diagnostic strategies.