Abstract
Artificial intelligence (AI) seeks to replicate human psychological capacities. This is making an ideal change for healthcare services with the rapid availability of healthcare data and the rapid progress of analytics technology. AI tools are being used to diagnose major diseases such as cancer, neurology, liver diseases, cardiology, and so on. For the classification of the disease, several classification and dimensionality reduction algorithms are used. In this chapter, recent literature based on deep learning technologies has been reviewed to pursue healthcare domains and also various applications, and challenges of AI and deep learning that are used in this field have been discussed. Different AI algorithms (Linear SVM, SVM Grid Search, KNN, Logistic Regression, Decision Tree, Bagging, Boosting) have also been discussed in a nutshell. To predict diseases, all these algorithms will be implemented using various medical datasets, and also a comparison of all these algorithms will be shown.
TopIntroduction
AI technologies have recently been entering in the healthcare sector from the management of healthcare till the discovery of new medicines. Regardless of whether it is impossible that the computers will totally change doctors and medical attendants, present day advancements are as of now changing the healthcare industry as we probably know it. At present, the healthcare industry is undoubtedly one of the foremost industries of Artificial Intelligence. There has been increasing enthusiasm for displaying and determining diseases over recent decades in which medical data set for healthcare are playing more and more significant roles. Here in this article, we’ve offered some of the features of the machine learning applications in healthcare that are as of now either being used or developed and have risen in the ongoing years. Figure 1 shows the brief overview of AI.
TopAi For Better Decision In Healthcare
In recent years, the role of artificial intelligence in health care has been very much discussed and at the same time no sign of adoption of this technology has also been given. Due to the security concerns, data integrities concern or unfortunate situation of many organizational silos, many health care analysts are still timid about trying different things with AI, which makes it impossible to share the data. However, the future of health care and the future of machine learning and artificial intelligence have a profound connection. Artificial intelligence in healthcare alludes to the utilization of complex algorithms intended to play out specific tasks in a computerized manner. Whenever analysts, specialists and researchers infuse information into PCs, the recently fabricated algorithms can survey, interpret and even propose solutions for complex therapeutic issues. In Figure 2, eight ways have been highlighted that show how this change is currently happening.
Figure 2.
Eight ways of AI in healthcare
TopApplication Of Ai In Healthcare
There is so much enthusiasm about how artificial intelligence (AI) is going to change the healthcare industry. And many AI technologies are helping people to make administrative and clinical health care processes effective. The findings show that artificial intelligence and machine learning are already delivering value in specific areas. Langley and Simon (Anju Gulia, A., Vohra, R., & Rani, P., 2014) found five major paradigms in the field of Machine learning: Neural networks, Instance based learning, Genetic algorithms, Inductive learning and analytical learning. Healthcare providers can use Ai to provide a more precise analysis and interpretation for initial diagnosis using available patient data for better treatment. Dozens of cases have been identified using artificial intelligence in the health care industry and these use cases have been structured in figure 3 which are used in the healthcare industry.
Figure 3.
AI Applications / Use cases in healthcares
Table 1.
This shows the top 10 AI applications that could change healthcare
Key Terms in this Chapter
Support Vector Machine: It is a linear model for classification and regression problems to solve linear and non-linear problems.
Disease Prediction: It is a way to recognize patient health by applying data mining and machine learning techniques on patient treatment history.
Artificial Intelligence: It refers to the imitation of human aptitude in machines that are programmed to think like humans.