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Extensive Review on the Role of Machine Learning for Multifactorial Genetic Disorders Prediction

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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.

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Authors and Affiliations

  1. Yogananda School of AI Computers and Data Science, Shoolini University, Solan, Himachal Pradesh, India

    Dahlak Daniel Solomon &  Sonia

  2. Electronics and Communication Engineering Department, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, 133207, India

    Karan Kumar

  3. Department of Computer Science & Engineering and Information Technology, Jaypee University of Information & Technology, Solan, Himachal Pradish, India

    Kushal Kanwar

  4. Department of Computer Science & Engineering, Rai University, Ahmedabad, Gujrat, India

    Sailesh Iyer

  5. School of Computer Science, FEIS, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE

    Manoj Kumar

  6. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Manoj Kumar

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  1. Dahlak Daniel Solomon
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  2. Sonia
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  3. Karan Kumar
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  4. Kushal Kanwar
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  5. Sailesh Iyer
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  6. Manoj Kumar
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Correspondence to Sonia or Karan Kumar.

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Solomon, D.D., Sonia, Kumar, K. et al. Extensive Review on the Role of Machine Learning for Multifactorial Genetic Disorders Prediction. Arch Computat Methods Eng 31, 623–640 (2024). https://doi.org/10.1007/s11831-023-09996-9

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  • DOI: https://doi.org/10.1007/s11831-023-09996-9

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