Synergistic Genomic Profiling and Predictive Modeling for Health Outcome Anticipation: A Holistic Strategy Leveraging Gene Expression Analysis

This paper presents a thorough way to disease prediction that integrates powerful machine learning algorithms with microarray analysis of cancer gene expression. The loading, recording, and cleaning of genetic microarray data is the primary stage in the study’s approach. A collection of genes crucial for illness prediction is discovered when traits are chosen based on statistical significance. The research evaluates how many classifiers, such as Gaussian Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi-layer Perceptron, and Extra Trees, are influenced by increasing the number of individual genes. The selecting of essential genes and models is lead by extensive evaluation methodologies and crossvalidation procedures. The research also provides a theoretical framework, underlining the relevance of gene expression patterns in understanding and identifying disorders. The relevance of feature selection and how it influences model performance are underlined in the research. It also looks at the selected strategies and how well they function with genetic data management. The methodology’s success is proved by a comparative analysis of error rates and the choice of the bestperforming software and gene set.