In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosis. The first two are relatively new research areas in databases, and the third one is an established subject in knowledge representation. We show how to obtain database repairs from causes, and the other way around. Causality problems are formulated as diagnosis problems, and the diagnoses provide causes and their responsibilities. The vast body of research on database repairs can be applied to the newer problems of computing actual causes for query answers and their responsibilities. These connections, which are interesting per se, allow us, after a transition -inspired by consistency-based diagnosis- to computational problems on hitting sets and vertex covers in hypergraphs, to obtain several new algorithmic and complexity results for database causality.

Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R. Examples of use of decision tress is predicting an email as spam or not spam, predicting of a tumor is cancerous or predicting a loan as a good or bad credit risk based on the factors in each of these. Generally, a model is created with observed data also called training data. Then a set of validation data is used to verify and improve the model.

Image: Doctors were significantly better at diagnosing conditions correctly when compared to a computer algorithms, an American research trial found. Doctors "vastly outperformed" computer algorithms when it comes to giving a correct diagnosis, according to an American research trial. In the investigation, 234 physicians correctly diagnosed patients 72% of the time, in comparison to the symptom checker app, Human Dx, which only managed to score 34%. Published earlier this month in'JAMA Internal Medicine', the study stated that it is thought to be the first direct comparison of diagnostic accuracy. Human Dx, is a web and app based platform on which clinicians can produce different outcomes for clinical case studies.

Hidden Decision Trees is a statistical and data mining methodology (just like logistic regression, SVM, neural networks or decision trees) to handle problems with large amounts of data, non-linearities and strongly correlated dependent variables. The technique is easy to implement in any programming language. It is more robust than decision trees or logistic regression, and help detect natural final nodes. Implementations typically rely heavily on large, granular hash tables. No decision tree is actually built (thus the name hidden decision trees), but the final output of an hidden decision tree procedure consists of a few hundred nodes from multiple non-overlapping small decision trees.

Welcome back for another edition of Under the Decision Tree. This week we had The Data Science Conference in Seattle and interesting articles that include teaching AI to be sarcastic, predictions of what AI will look like in 2030, and much more. Please send any suggestions to: Decision Tree We would love to hear from you.

Welcome back for another edition of Under the Decision Tree. This week we had everything from machine learning being applied to cucumber farming, to major tech companies joining up to tackle the ethics of machine learning. Please send any suggestions to: Decision Tree We would love to hear from you.

Decision trees are used extensively in machine learning because they are easy to use, easy to interpret, and easy to operationalize. KD Nuggets, one of the most respected sites for data science and machine learning, recently published an article that identified decision trees as a "top 10" algorithm for machine learning. If you are new to machine learning, some of these concepts may be unfamiliar. The goal of this blog is to provide you with the basics of decision trees using Talend and Apache Spark. If you want to learn more about advanced analytics, please see the references section below.(2)

In the future, speaking may be all that's required to diagnose health issues. Beyond Verbal, an Israeli company specializing in analyzing emotion from vocal intonation, is set to launch a platform today that could be the first step in doing just that. Momentum by TNW is our New York technology event for anyone interested in helping their company grow. The Beyond mHealth Research Platform could usher in a brave new world of healthcare research by correlating distinct vocal features that are mostly imperceptible to humans. In doing so, these vocal'biomarkers' could alert your physician to the presence of anything from depression and stress to heart disease. This sort of early detection could prove to be the difference between life and death.

This book is an excellent source for machine learning in computer-aided diagnosis which is a rapidly growing area in medicine, especially medical imaging. It comprehensively covers recent advances in technologies and applications in major areas in the field of computer-aided diagnosis. The editor and contributors are very famous researchers in the field. It is an excellent reference book.

Welcome back for another edition of Under the Decision Tree. As usual there were quite a number of interesting stories focused on machine learning and AI. One particularly interesting topic this week was Micorsoft and its efforts in cancer research. There are two conferences starting on Monday next week. Please send any suggestions to: Decision Tree We would love to hear from you.