The Role of Artificial Intelligence and Machine Learning Methodologies in Bioinformatics

Medical classification processing is one of the complex systems for analysis and still provides meaningful information. This meaningful information gives decision-maker to pathologists, which helps them in decision-making in proper diagnosis and timely treatment. X-ray imaging, ultrasound images, MRI Images, histopathological imaging, and other types of medical image processing is currently used to diagnose and detect numerous disorders. In recent days, a computer-assisted grading system has been designed to support healthcare professionals, particularly radiologists and clinicians, in interpreting medical images and making diagnostic decisions. These systems utilize advanced algorithms and techniques from fields to analyze medical images and provide additional information to assist in diagnosis. This system can aid in the detection of abnormalities, the classification of diseases, and the assessment of treatment response, among other clinical tasks. They serve as decision support tools, providing clinicians with quantitative measurements, visual cues, and probability estimates to enhance diagnostic accuracy and improve patient care. So, early identification of these biased tissues, followed by proper treatment, becomes vital to increasing patients’ chance of remission and prolonging their lives. The purpose of the present research is to increase the accuracy of machine learning approaches for disease diagnosis using ML techniques.