Machine Learning in Medical Science
Introduction
Disease identification and diagnosis of ailments is at the forefront of ML research in medicine. One of the chief ML applications in healthcare is the identification and diagnosis of diseases and ailments which are otherwise considered hard-to-diagnose. This can include anything from cancers which are tough to catch during the initial stages, to other genetic diseases.Machine learning and deep learning are both responsible for the breakthrough technology called Computer Vision. This has found acceptance in the Inner Eye initiative developed by Microsoft which works on image diagnostic tools for image analysis. As machine learning becomes more accessible and as they grow in their explanatory capacity, expect to see more data sources from varied medical imagery become a part of this AI-driven diagnostic process.
ML in Health Care
The main role of machine learning in healthcare is to ease processes to save time, effort, and money.Machine Learning is used across many spheres around the world. The healthcare industry is no exception. Machine Learning can play an essential role in predicting presence/absence of Loco-motor disorders, Heart diseases and more. Such information, if predicted well in advance, can provide important insights to doctors who can then adapt their diagnosis and treatment per patient basis.Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient.We can use Classification and Regression Trees (CART) to generate the fuzzy rules to be used in the knowledge-based system. We test our proposed method on several public medical data sets. Cleveland and Parkinson’s monitoring data sets show that proposed method remarkably improves the diseases prediction accuracy. The results showed that the combination of fuzzy rule-based, CART with noise removal and clustering techniques can be effective in diseases prediction from real-world medical data sets.The data set is available through the University of California, Irvine Machine learning repository.Machine learning and artificial intelligence is going to have a dramatic impact on the health field; as a result, familiarizing yourself with the data processing techniques appropriate for numerical health data and the most widely used algorithms for classification tasks is an incredibly valuable use of your time.
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