Advancements in Data Mining and Machine Learning Techniques for Predicting Human Diseases: A Comprehensive Review

Abstract

Machine learning can assist healthcare providers in a variety of patient care and intelligent health systems. From machine learning to deep learning, these technologies are widely used in medical diagnosis, drug discovery, and patient risk identification. The complexity of diverse disease mechanisms and basic symptoms in the global patient population poses significant challenges in developing early diagnosis tools and effective treatments. Machine learning (ML), a subfield of artificial intelligence (AI), empowers researchers, doctors, physicians, and patients to address various medical diagnostic problems. Drawing on extensive research, this review study elucidates the usefulness of machine learning in the early recognition of numerous diseases. The study encompasses a comprehensive survey of machine learning-based approaches for diagnosing diseases such as cancer, diabetes, chronic heart disease, tuberculosis, hypertension, skin conditions, brain tumors, and liver disease. This comprehensive examination of machine learning in healthcare provides valuable insights for researchers and practitioners, paving the way for improved diagnosis accuracy and more effective treatment strategies.