One of the most intuitive and popular methods of data mining that provides explicit rules for classification and copes well with heterogeneous data, missing data, and nonlinear effects is decision tree. It predicts the target value of an item by mapping observations about the item. You can perform either classification or regression tasks here. For example, identifying fraudulent transactions using credit cards would be a classification task while forecasting prices of stock would be regression task. Decision tree technique is used to detect the criteria for dividing individual items of a group into n predetermined classes (Often, n 2 represents a balanced tree, which means a largest of two child nodes for each parent node.) Firstly, a variable is taken as the root node.

It might also be able to help doctors diagnose and treat diseases: Microsoft and Adaptive Biotechnologies have teamed up to create an AI tool that they are hoping can decode--or read--the immune system. The companies hope to pair advances in AI and machine learning with recent breakthroughs in biotechnology to map out the immune system and tap into the body's impressive diagnostic system. If you haven't watched this episode of The Magic Schoolbus in a while, the human immune system, when functioning properly, is incredible at diagnosing and treating illness and injury--whether a paper cut, fever, or raging hangover. Microsoft and Adaptive Biotechnologies want to figure out how it works, and hopefully, make it work for them and all of humanity. Per a press release, their goal is to "create a universal blood test that reads a person's immune system to detect a wide variety of diseases including infections, cancers and autoimmune disorders in their earliest stage, when they can be most effectively diagnosed and treated."

As anyone who has read the news lately knows, artificial intelligence is a lot more than just an under-appreciated Jude Law film. It is the future of farming, it writes better Yelp reviews than you, it makes groovy special effects, it can nose out corruption, it can furnish your home, and even beat you at video games. It might also be able to help doctors diagnose and treat diseases: Microsoft and Adaptive Biotechnologies have teamed up to create an AI tool that they are hoping can decode--or read--the immune system. The companies hope to pair advances in AI and machine learning with recent breakthroughs in biotechnology to map out the immune system and tap into the body's impressive diagnostic system. If you haven't watched this episode of The Magic Schoolbus in a while, the human immune system, when functioning properly, is incredible at diagnosing and treating illness and injury--whether a paper cut, fever, or raging hangover.

The human immune system is an astonishing diagnostic system, continuously adapting itself to detect any signal of disease in the body. Essentially, the state of the immune system tells a story about virtually everything affecting a person's health. It may sound like science fiction, but what if we could "read" this story? Our scientific understanding of human health would be fundamentally advanced. And more importantly, this would provide a foundation for a new generation of precise medical diagnostic and treatment options. Amazingly, this isn't just science fiction, but can be science fact.

The First National Aeronautics and Space Administration (NASA) Workshop on Monitoring and Diagnosis was held in Pasadena, California, from 15 to 17 January 1992. The workshop brought together individuals from NASA centers, academia, and aerospace who have a common interest in AIbased approaches to monitoring and diagnosis technology. The workshop was intended to promote familiarity, discussion, and collaboration among the research, development, and user communities. The First National Aeronautics and Space Administration (NASA) Workshop on Monitoring and Diagnosis was held in Pasadena, California, from 15 to 17 January 1992. The workshop was hosted by the Jet Propulsion Laboratory (JPL) and took place at the Ritz-Carlton Huntington Hotel.

Among the difficulties in evaluating AItype medical diagnosis systems are: the intermediate conclusions of the AI system need to be looked at in addition to the "final" answer; the "superhuman human" fallacy must be resisted; both pro-and anti-computer biases during evaluation must be guarded against; and methods for estimating how the approach will scale upwards to larger domains are needed We propose a type of Turing test for the evaluation problem, designed to provide some protection against the problems listed above We propose to measure both the accuracy of diagnosis and the structure of reasoning, the latter with a view to gauging how well the system will scale up A staple of many of the evaluations of AI systems that have so far been conducted (Colby, Hilf, Weber, 81 Kraemer, 1972; Yu et al, 1979) is a central idea from a well-known proposal to evaluate AI systems: The Turing Test (Turing, 1963) The meat of the idea is to see if a neutral observer, given a set of performances on a task, some by a machine and others by humans, but unlabelled as to authorship, could identify, better than chance, which were machine and which were human-produced. Note that this really attempts to answer the question, "DO we know how to design a machine to perform a task which until now required human intelligence?", The latter question subsumes the former in a sense: because the machine not performing well in comparison to a human would presumably increase the cost significantly. In this paper I follow tradition and consider the evaluation of AI systems for medical diagnosis from the viewpoint of the first question above. The proposed procedure is also a variant of Turing's Test.

Most high-performance expert systems rely primarily on an ability to represent surface knowledge about associations between observable evidence or data, on the one hand, and hypotheses or classifications of interest, on the other. Although the present generation of practical systems shows that this architectural style can be pushed quite far, the limitations of current systems motivate a search for representations that would allow expert systems to move beyond the prevalent "symptom-disease" style. One approach that appears promising is to couple a rule-based or associational system module with some other computational model of the phenomenon or domain of interest. According to this approach, the domain knowledge captured in the second model would be selected to complement the associational knowledge represented in the first module. Simulation models have been especially attractive choices for the complementary representation because of the causal relations embedded in them (Brown & Burton, 1975; Cuena, 1983).

Automated diagnosis is an important AI problem not only for its potential practical applications but also because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Current research in this area addresses many problems, including managing and structuring probabilistic information, modeling physical systems, reasoning with defeasible assumptions, and interleaving deliberation and action. Furthermore, diagnosis programs must face these problems in contexts where scaling up to deal with cases of realistic size results in daunting combinatorics. This article presents these and other issues as discussed at the First International Workshop on Principles of Diagnosis. Diagnosis has historically provided an obliging rock for each succeeding generation of AI researchers to blunt their axes on.

I report on my experience over the past few years in introducing automated, model-based diagnostic technologies into industrial settings. In particular, I discuss the competition that this technology has been receiving from handcrafted, rule-based diagnostic systems that has set some high standards that must be met by model-based systems before they can be viewed as viable alternatives. The battle between model-based and rulebased approaches to diagnosis has been over in the academic literature for many years, but the situation is different in industry where rule-based systems are dominant and appear to be attractive given the considerations of efficiency, embeddability, and cost effectiveness. Traditionally, industrial diagnostic systems have been handcrafted to reflect the knowledge of a domain expert. They take the form of if-then rules that associate certain forms of abnormal system behavior with faults that could have caused this behavior.

The eighteenth annual International Workshop on Principles of Diagnosis was held in Nashville, Tennessee, May 29-31, 2007. Papers presented at the workshop covered a variety of theories, principles, and computational techniques for diagnosis, monitoring, testing, reconfiguration, fault-adaptive control, and repair of complex systems. Before deployment they are subjected to strict testing and validation. Although these procedures reduce the likelihood of initial system failures, degradation and faults in system components still occur because of wear and tear from sustained operations. Industry sources, service agencies, and the military report that down time, due to maintenance and repairs of equipment, is still a significant cost of daily operations.