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Machine Learning in Healthcare

As per the definition of machine learning, it is a field of computer science that gives computer systems the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. The later part of the definition, “without being explicitly programmed,” is controversial, as there are hardly any computers that do not require programming to learn. But what this could mean for applying machine learning in business is the use of supervised and unsupervised machine learning techniques.

Supervised learning techniques are the ones where the computer needs references of past data and explicit categorization and explanation of patterns, trends, and facts from it. However, for unsupervised learning this is not a requirement; we let the computer learn on its own to find the patterns, trends, and facts. This is also known as auto-discovery or auto-data-mining.

unsupervised learning, you can say that the computer program is not being explicitly programmed to learn. It is learning on its own by discovering the facts, patterns, and trends. But we do program it by selecting the algorithms it will use to discover them. It does not select the algorithms by itself.

How Machine Learning Is Transforming Healthcare ?

Let us now look at some of the ways that classification machine learning is transforming the healthcare segment of business,there are some reasons why we need machines to do this work more efficiently:
• High volume of imaging data with increased patients.
• Inability of healthcare professionals to link and see the big picture from imaging data. Machines can help them by assessing large numbers of image datasets and determine whether there are any patterns or any connections among groups of patients or groups of localities, for example.
• Drug discovery is a very key area for the healthcare industry. Research in the pharmaceutical companies for diseases like cancer or HIV is continuously happening.
• Stress on doctors due to high volumes makes them more error-prone. Machines can handle large sets of imaging data with a lower error rate.
• Replace doctors or specialist at times of their absence. This is a key operation that a machine can do—when a specialist is not available, it can replace the human specialist and provide diagnosis in even critical cases.

Healthcare machine learning applications:-

  1. Disease identification
  2. Personalized medicine
  3. Surgical robotics Automation
  4. Drug discovery
  5. Radiology Digital health records
  6. Epidemic outbreak prediction
  7. Drug manufacturing
  8. Clinical trial research

Key reasons why we need disease diagnosis to be done by machines are:-

• Every year 195,000 patients in the US die of medical diagnostic error.
• To increase the value of healthcare to its patients, it is an absolute must to decrease the cost of diagnosis given to a patient.
• Inability of a human being to process huge amounts of information, analyze it, and apply it to a particular disease diagnosis.
• Accuracy in diagnosis is critical, as an incorrect diagnosis increases the cost of healthcare for the patient. Using machine learning, we can track, measure, optimize, learn, and improve based on feedback on the accuracy of diagnosis by a robot or a machine.

Life Cycle of Machine Learning Development

Before proceeding to look at data sets and building machine learning models, let us first look at what a typical machine learning life cycle is and learn its implementation within our short applications. This is a generic life cycle model that I am presenting to the reader; this can be applied to any machine learning application development and not just in the healthcare industry but also in retail, finance,or other sectors.

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In this article i have cover how Machine Learning life cycle, applications and advantages in Health Care dentistry and with the help of IoT we can automate all health care services.Read more Article on ... OnClick360

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