Extensive Review on the Role of Machine Learning for Multifactorial Genetic Disorders Prediction
Publication Name
Archives of Computational Methods in Engineering
Abstract
The culture of employing machine learning driven assistance and decision making is currently adopted by a variety of industries. Artificial intelligence encompasses a wide range of learning technologies, including machine learning. One of the most significant and advantageous industries is the medical and health care field. In the field of healthcare, machine learning is currently begin implemented in practically every facet, including clinical research, early-stage disease identification, medication discovery, and virtual support. The prediction and identification of complex disease as soon as possible is one of the main aspects that is currently using data driven approaches and which shows that machine learning is playing a vital role by helping to make a real time difference in the effort to save human lives. Even though disease prediction and detection can be valuable and is one of the most crucial roles that machine learning adaptation plays in the health industry, doing so can be difficult and requires a significant amount of effort due to the complexity and behavior of diseases. Because they stem from a combination of environmental variables and anomalies in many genes, multifactorial inherited genetic disorders are the hardest to predict and diagnose. By presenting a brief explanation on characterizations, stages, and basic concepts about ten well-known disease that fall within the category of multi-factorial genetic condition, this comprehensive research review discusses the current trends on adopting various machine learning approaches on Heart diseases, Alzheimer, Obesity, Diabetes and Blood pressure.
Open Access Status
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