Integrating and Interpreting Biomedical Analysis: A Comprehensive Analysis of Machine Learning Algorithms for Precision Medicine

• Published in: International Conference on Computing, Communication and Automation (Online) pp. 1 - 6
• Main Authors: Sasikala, G., J, Sofia Bobby, A, Dhanamathi, Annapoorani, C. L., M, Prathisha, Mythily, V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.11.2024
• Subjects: Bioinformatics; Biological system modeling; Clinical Records; Disease Diagnosis; Genomics; Machine learning algorithms; Personalized Treatment Planning; Planning; Precision medicine; Prediction algorithms; Proteomics; Supervised learning; Treatment Response Prediction; Unsupervised learning
• ISSN: 2642-7354
• DOI: 10.1109/ICACCM61117.2024.11059016

A new era in healthcare has begun with precision medicine, which tailors medications to the specific characteristics of each individual patient. This shift is being driven by the advent of advanced machine learning (ML) algorithms that can parse and make sense of biological data culled from diverse sources like clinical records, proteomics, and genomes. This study aims to give a thorough evaluation of ML algorithms employed in biomedical analysis for precision medicine, with a particular emphasis on these algorithms' integration, interpretation, and practical uses. In this first step, we take stock of biological data and its current state, drawing attention to issues like data volume, privacy concerns, and data heterogeneity that pose obstacles to its integration. We continue by taking a look at the most recent developments in supervised and unsupervised learning as well as deep learning algorithms used in biological analysis. In the context of precision medicine, we discuss the pros and cons of these algorithms and emphasise their ability to handle complex data and extract valuable insights. Methods for feature selection, data preprocessing, and model validation are among the topics covered in this investigation of how to integrate ML algorithms with biological data. To make sure that healthcare providers and patients can understand and trust the results of machine learning (ML) models, we investigate how explainable AI (XAI) can help interpret these decisions. We also provide case examples that show how ML algorithms have been used for precision medicine to diagnose diseases, predict how treatments will work, and create individualised treatment plans. These examples show how ML has the ability to revolutionise healthcare by enhancing patient outcomes while decreasing expenses. Lastly, we discuss potential future developments and areas for future research in the field, such as improving XAI methods, integrating multi-omics data, and creating more sophisticated ML models. The research highlights the significance of integrating knowledge from several disciplines to progress precision medicine, specifically computer science, data science, and biomedical sciences.