This article is by Jitesh Shah, a data & stats jockey in perpetual beta, located in Fremont, California. This article includes the data set and Python code. Wouldn't it be nice if defects and product failures can be predicted in advance. We've got the data on attributes and design features and manufacturing processes that come together and creates that product and we have defect and failure rate data so all we got to do is connect the two and use that to predict which set of features and attributes and processes in combination cause these defects. That was probably a non-trivial endeavor in the past but now with the ability to store and process vast amounts of data (no secret there), no big deal.

Radiation therapy is an integral part of many cancer treatments. Ideally, doses are focused on the observable tumor while leaving surrounding organs unaffected, but determining the figuration of tumors and organs-at-risk is done manually--a time consuming and, at times, imprecise task for radiologists. A team of Chinese researchers developed a machine learning technique--closed polygonal line and backpropagation neural network model (CPL-BNNM)--for accurately detecting smooth lung contours in 3D-CT scans that is more efficient than manually determining such information and superior to currently used algorithms. "The important information for organ diseases can be quantitatively provided by the clinical images, while quantification is often manually implemented in some clinics," wrote Tao Peng, with the School of Computer Science & Technology at Soochow University. "In order to speed up the manual task and reduce workload, combining computer-aided diagnosis with automatic detection method is becoming a research hotspot."

In the coming decades, diagnostic medicine will likely change dramatically. Perhaps the most conspicuous change will be the arrival of artificial intelligence (AI) for faster and better care. AI is not pitting man against machine. It is, in fact, a way to ease the physician's burden and expand the possibilities of treatment. The administrative aspect of practicing medicine can be overwhelming, to say the least, and AI offers the chance to sort through large amounts of information quickly and accurately.

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I did a series of blog posts on different machine learning techniques recently, which sparked a lot of interest. You can see part 1, part 2, and part 3 if you want to learn about classification, clustering, regression, and so on. In that series I was careful to differentiate between a general technique and a specific algorithm like decision trees. Classification, for example, is a general technique used to identify members of a known class like fraudulent transactions, bananas, or high value customers. Read this machine learning post if you need a refresher or are wondering quite what bananas have to do with machine learning.

Melanoma is the deadliest form of skin cancer. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to investigate the impact of segmentation and classifier. The unexpected small impact of automatic versus semi-automatic/manual segmentation suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

On the move: Dynatrace gets the green light in AI innovation, next stop the Internet of Things. Back with a bang on day two, Dynatrace exceeded expectations yet again with a whole host of announcements. With the same ethos in mind, the performance management company looked at the hype capabilities in the IT industry – artificial intelligence (AI) and cloud computing. Once again, perfection was the goal for Bernd Greifeneder, CTO at Dynatrace. Keeping customers at the heart of all its operations, Dynatrace's virtual assistant Davis powered today's problem solving performance announcement as it looks to disrupt the Internet of Things (IoT) market.

If you've been reading our blog regularly, you have noticed that we mention decision trees as a modeling tool and have seen us use a few examples of them to illustrate our points. This month, we've decided to go more in depth on decision trees--below is a simplified, yet comprehensive, description of what they are, why we use them, how we build them, and why we love them. A decision tree is a popular method of creating and visualizing predictive models and algorithms. You may be most familiar with decision trees in the context of flow charts. Starting at the top, you answer questions, which lead you to subsequent questions.

In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix . By the end of this course, you will be able to effectively summarize your data, visualize your data, detect and eliminate missing values, predict futures outcomes using analytical techniques described above, construct a confusion matrix, import and export a data.