Revolutionizing Healthcare by Unleashing the Power of Machine Learning in Diagnosis and Treatment

Machine learning (ML) is a versatile technology that has the potential to revolutionize various industries. ML can predict future trends in customer expectations that allow organizations to develop new products accordingly. ML is a crucial field of data science that uses different algorithms to predict insights and improve decision-making. The widespread acceptance of ML algorithms ML can provide helpful information using the enormous volume of health data generated regularly. Quicker diagnoses by doctors can be delivered by adopting ML techniques that can bring down medical charges and applying pattern identification algorithms to examine medical images. Every technology brings its challenges; in the same way, ML also has several challenges in healthcare that need to be acknowledged before we witness complete automation in medical diagnosis. People are still forbidden to share their personal information with intermediaries for treatment. Medical record governance is essential to ensure that health records are not missed. Manual diagnosis often goes in the wrong direction, as doctors are also human. Lack of communication between medical workers and patients, considering the insufficient data to diagnose disease, sometimes results in deteriorating health conditions. This paper deals with an introduction to machine learning. These ML algorithms are widely used for health diagnosis, a comparison analysis of literature work that has been done so far, existing challenges of the healthcare system, healthcare industry using machine learning applications, real-life use cases, practical implementation of disease prediction, and conclusion with its future scope.

@article{Gupta2024,
  title     = {Revolutionizing Healthcare by Unleashing the Power of Machine Learning in Diagnosis and Treatment},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Medini Gupta and Sarvesh Tanwar and Salil Bharany and Faisal Binzagr and Hadia Abdelgader Osman and Ashraf Osman Ibrahim and Samsul Ariffin Abdul Karim},
  doi       = {10.14569/IJACSA.2024.01503111},
  url       = {https://doi.org/10.14569/IJACSA.2024.01503111}
}