Bridging the gap between Machine Learning and Medicine: Understanding recent advances in machine learning-based disease diagnosis systems. - Iqra Nissar; Shahzad Alam; Sarfaraz Masood (Journal of ICT Research and Applications, 2023)
There is an enormous increase in digitized health records which provides an opportunity in improving healthcare for clinicians as well as patients in terms of both discovery and practical implications. However, to best use this data, high computational techniques are required that can handle large and complex datasets. Artificial Intelligence and recent advances in deep learning have become a vital aspect of our life and its positive results have been very astonishing. Medicine has been very receptive to the benefits of machine learning, as it adopts the rapidly evolving technology at a faster pace than the other sectors such as finance, transport, and entertainment. Machine learning, studies methods that are used to train the models which facilitate them to identify the useful patterns in data and improve their learning upon experience. Health informatics uses the probabilistic information effectively for decision making. Machine learning and health informatics together have a greater potential to increase the quality and efficiency of healthcare. Recently, researchers have demonstrated that machine learning can create algorithms that can perform better than physicians. It provides actionable insights, thus improving the patient's risk score and streamlining the hospital operations. The implementation of machine learning for medical notes has revolutionized the patient’s health outcome. The applications of machine learning have excellent capabilities in healthcare and can be seen in various domains: In the prediction of different diseases like neurological disorders, cardiovascular diseases, and also in drug discovery, prognosis, personalized medicine, medical image analysis, and diagnosis in various types of cancers for example lung cancer, breast cancer, skin cancer, etc. This paper provides a comprehensive overview of how machine learning algorithms have been deployed for creating the efficient decision support system for healthcare analysis.