5 AN OVERVIEW OF MACHINE LEARNING AND ITS APPLICATIONS IN HEALTH CARE
Shimoo Firdous
Doctoral Research Scholar, Department of Computer Science, Bhagwant University Ajmer, India, 305004
Dr. Kalpana Sharma
Assistant Professor, Department of Computer Science, Bhagwant University Ajmer, India, 305004
Abstract- In recent years, there has been a greater emphasis on using machine learning to address complicated problems in a variety of disciplines. Similarly, machine learning usage in healthcare is increasing, fundamentally altering the face of health care delivery. Machine learning's fundamental goal is to deliver more positive results with more exact forecasts.
The goal of this study is to show how machine learning technologies have become an important part of developing more effective and comprehensive methods that lead to better patient outcomes and enhanced healthcare practices. We'll also talk about the benefits of using machine learning technologies in healthcare and why it's necessary.
Keywords: Machine learning, Healthcare, Machine learning Applications, Accuracy.
1 INTRODUCTION
Machine learning is one of the most commonly used artificial intelligence technologies in recent years. Machine learning is an artificial intelligence application that allows robots to learn from algorithms to obtain human-like intelligence and the ability to conduct actions without having to be explicitly programmed. Algorithms are developed in machine learning based on the machines' previous experience, or we may say that machine learning learns from itself through developed algorithms. The fundamental benefit of employing machine learning algorithms is that it improves on previously created algorithms, resulting in improved prediction or decision, which can then be applied to the real world. In other words, the purpose of machine learning is to comprehend the structure of data to make accurate predictions based on the data's attributes. Supervised learning, Semi-supervised learning, Unsupervised Learning, and Reinforcement learning are the four types of machine learning.
1.1 Working of Machine Learning
Machine learning algorithms are trained by using large training data sets to create a model.
Once a model is created, a prediction is made accordingly. When new data is given as input to the machine learning algorithm, it makes a prediction based on the model. After that prediction is measured for accuracy. If the accuracy is satisfactory, the machine learning algorithm is deployed. If the accuracy is not satisfactory, the machine is again trained by machine learning algorithms and again with an augmented training data set.
2 TRADITIONAL PROGRAMMING VS MACHINE LEARNING
Both traditional programming and machine learning are aimed at resolving a problem or issue, but the key distinction between the two is how they are implemented. Machine learning follows a data-driven approach, whereas traditional programming is heavily reliant on the developer's creativity to create an algorithm that solves the problem. In simpler terms, a traditional data analysis strategy begins with the model as an input to the machine, whereas a machine learning approach begins with the data and produces a model that can subsequently be applied to new data.
Traditional Approach
Model Result Data Computation
6 Machine Learning Approach
Data Model
2.1 Role of Machine Learning in Health Departments
When it comes to healthcare departments, machine learning produces better results. This is due to its increased efficiency and ability to make quick decisions. For healthcare services, machine learning systems can be designed that can change disease diagnosis and treatment, ensuring that patients receive the proper treatment at the right time.
The use of machine learning in healthcare has resulted in several positive advances, including:
I. Body scanning can be used for imaging and diagnostics.
II. Using surgical robots, operating on a body becomes easier and more comfortable.
III. Cancer treatment has become a lot easier thanks to these machines that show the effects of radiation on the body.
IV. Because these machines operate with greater precision and accuracy, fewer complications are expected.
V. Detecting drugs and medicines is becoming easier since machines can predict how a person will react to particular substances and situations.
2.2 Why there is a need for Machine Learning in Health Care
While the capacity to capture vast volumes of information on individual patients is transforming the healthcare industry, the massive amount of data being collected is impossible for humans to analyze. Machine learning allows healthcare providers to advance toward individualized care, often known as precision medicine, by automatically finding patterns and reasoning about data. Every year, a large number of foreign citizens die as a result of medical errors reported in hospitals. Machine learning can be employed to a greater extent in this case since it offers scalability, speed accuracy (to a greater extent), and the prediction of many outcomes in the health care area. Both the doctor and the machine are attempting to complete the same task. A doctor examines a patient's symptoms and determines what type of ailment he or she is suffering from; the machine, on the other hand, tries to identify the disease using the training samples provided to it or its previous experience. As a result, using machine learning in healthcare will be quite beneficial.
2.3 Applications of Machine Learning in Health Care
ML is being used to investigate the significance of clinical factors and their combinations for prognosis, such as illness progression prediction, medical knowledge extraction for outcome research, therapeutic planning and support, and overall patient care. ML is also utilized for data analysis, such as detecting regularities in data by effectively dealing with defective data, interpretation of continuous data used in the Intensive Care Unit, and intelligent alarms, all of which result in effective and efficient monitoring. It is suggested that the successful deployment of machine learning methods can aid in the integration of computer- based systems in the healthcare environment, allowing medical specialists' work to be facilitated and enhanced, thus improving the efficiency and quality of medical care. We've compiled a list of some of the most important machine learning applications in medicine.
Medical diagnostic reasoning is a critical area of intelligent systems application.
2.4 Diagnosis in Imaging
A prominent application area that provides significant support in medical diagnosis is computer-based medical picture interpretation systems. X-rays, computer tomography (CT) scans, ultrasound, magnetic resonance imaging (MRI), and nuclear medicine imaging, particularly Positron-Emission Tomography (PET), are all examples of imaging. Each uses a different technology and is employed differently. X-Ray scans, as the name suggests, use X- rays to photograph dense material such as bones, cancers, and so on. CT scans combine
Computation
7 several X-ray images from various sources, which are then computer-processed to produce cross-sectional images of bones, tissues, cancers, and other structures within the human body. CT scans produce more detailed 360-degree images, which are frequently enhanced with contrast dyes. Nuclear imaging is a patient swallowing or being injected with a radioactive tracer that releases gamma rays that are detected by a scanner or a gamma camera, resulting in images of bones and organ functions. It's utilized to examine organ function, blood flow, bone anomalies caused by cancer lesions or infections, inspect therapies, and so on. MRI uses strong magnetic fields and radio waves to create images of organs, bones, and soft tissue. MRI does not employ ionizing radiation, unlike X-rays and CT scans. For individuals with implants, MRI scans are not fully safe. . For bone structures, CT scans are preferable, while MRIs are favored for soft tissues such as the brain.
Ultrasounds provide images of organs, fetuses, tissues, and other moving objects using high-frequency sound waves. The interpretation of these photographs necessitates the use of qualified personnel. In some circumstances, ultrasounds necessitate the use of specialized probes.
2.5 Drug Discovery
Drug development Machine learning can also be utilized in the medical field to discover novel medications. Google and IBM have previously developed a machine learning framework to assist healthcare professionals in uncovering new therapy options for patients.
2.6 Disease Identification/Diagnosis
A modern human doctor is unable to recollect all of the facts required to give an accurate and timely diagnosis. No doctor can understand every facet of medical care and recall every detail of similar instances in today's world of textbooks, research articles, and case studies.
However, rather than depending on far more limited human knowledge, it is possible to provide an AI-based system with relevant facts and let the machine move through the vast database. Machine learning in healthcare can quickly and accurately identify a variety of disorders. Machine learning can also be used to diagnose and treat patients in a variety of medical fields.
2.7 Behavioral Modification
Micro biosensors and devices, as well as mobile apps with more advanced health measuring tools and remote monitoring capabilities, will proliferate in the coming decade, providing even more data to aid R&D and improve treatment efficacy. This type of information will not only assist individuals to improve their health but will also help cut total healthcare expenses if more patients stick to their recommended drug or treatment plans.
3 CONCLUSION
Machine learning is currently one of the most rapidly developing technologies. Within a few years, it will have a significant impact on healthcare. It has a wide range of applications in the medical profession, which can take advantage of many of the machine learning qualities because they are so closely linked. Machine learning can be used to generate robust risk models using healthcare data. Machine learning applications can improve existing applications' efficiency and precision. These technologies will enable more patients to be served in less time, while also improving healthcare outcomes and lowering healthcare costs.
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