Predicting Treatment and Outcome of Cancer Genomics using Machine Learning Algorithm

Abstract

Cancer genomics has revolutionized personalized medicine by enabling targeted therapies based on an individual’s genetic profile. The outcomes of cancer treatment show considerable variation because of multiple factors between genetic mutations and patient-specific characteristics and tumor heterogeneity. Unsupervised learning methods inside machine learning systems provide effective research tools to discover covert patterns in substantial genomic data. The research evaluates the K-means clustering algorithm to forecast cancer genomics treatment responses and clinical end results. Clinical subgrouping of patients through genomic profiling of gene expressions mechanisms and mutational patterns aims to discover distinctive biological groups with different treatment outcomes. The proposed method combines Principal component Analysis (PCA) and (t-SNE) for dimensionality reduction of high-dimensional genomic data because it enhances clustering results. The K-means clustering procedure sorts patients into specific groups according to their genetic relationships. The arranged clusters help researchers detect patterns regarding survival outcomes together with drug responsiveness and tumor staging development. K-means clustering produces effective patient stratification that creates clinical subgroups for better individual treatment approaches based on preliminary findings. The model achieves better predictive results through combining multi-omics data which includes both transcriptomics and proteomics. Improvements are necessary to solve key issues regarding cluster selection optimization and interpretability problems related to features and class unbalance. The model achieves better predictive results through combining multi-omics data which includes both transcriptomics and proteomics. Improvements are necessary to solve key issues regarding cluster selection optimization and interpretability problems related to features and class unbalance. The research demonstrates how unsupervised learning techniques enable precision oncology by developing data-based methods for better treatment planning decisions. Future research will investigate the creation of clustered approaches between K-means and Random Forest (RF) for boosting cluster effectiveness and improving therapy prediction results in different cancer types.