A Research on Machine Learning Classifier Configuration for Medical Services Applications

Abstract

Machine learning (ML) techniques have gained significant prominence in the field of medical diagnostics for prediction and classification tasks. These techniques have proven invaluable in accurately and efficiently identifying illnesses for sickness diagnosis, leading to improved patient care. The advancements in systems and equipment utilized in healthcare have contributed to the increasing average human longevity. However, numerous challenges and issues persist within modern healthcare systems, including inaccurate patient information, data privacy concerns, lack of correct data, limited medical knowledge, and the use of classifiers for generating predictions, among others. To overcome these challenges, a range of sickness detection and prediction systems have been developed. Expert systems utilize knowledge-based rules and decision trees to aid in diagnosing diseases, while clinical prediction systems integrate patient data and evidence-based guidelines to provide real-time decision support. Decision support systems combine ML algorithms with clinical data to assist in diagnosis and treatment planning. Personal health record systems empower individuals to manage their health data and contribute to research efforts. Efforts are being made to address the challenges associated with inaccurate patient information through improved data collection processes, data interoperability, and data quality assurance measures. Data privacy concerns are being addressed by implementing robust security protocols and data anonymization techniques. To mitigate the lack of correct data, initiatives for data-sharing collaborations, standardized datasets, and data augmentation techniques are being pursued. Enhancing medical knowledge and interpretability of ML models are active areas of research, aiming to provide transparent and understandable insights into the decision-making process.