Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction

TL;DR: Results suggest that sampling Re-RX with J48graft provides more accurate, concise, and interpretable extracted rules than previous algorithms, and is therefore more suitable for medical decision making, including the diagnosis of T2DM.

TL;DR: The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test.

TL;DR: We argue that instance-based learning satisfies the conditions for intuitive decision-making described in Kahneman and Klein (2009) , separates automatic from deliberative processes, and provides a general mechanism for the study of intuitive decision making.

TL;DR: In this paper , a review of ML algorithms, applications, techniques, opportunities, and challenges for the healthcare sector is presented, which fills a research gap for efficient use of machine learning algorithms and applications.

TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.

TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.