Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13-year follow up data set from MESA (the Multi-Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD-free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10-year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of "All CVD " events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short-term CVD risk prediction

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Rank-based Decomposable Losses in Machine Learning: A Survey

Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses vs. aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

An experimental investigation of calibration techniques for imbalanced data

Calibration is a technique used to obtain accurate probability estimation for classification problems in real applications. Class imbalance can create considerable challenges in obtaining accurate probabilities for calibration methods. However, previous research has paid little attention to this issue. In this paper, we present an experimental investigation of some prevailing calibration methods in different imbalance scenarios. Several performance metrics are considered to evaluate different aspects of calibration performance. The experimental results show that the performance of different calibration techniques depends on the metrics and the degree of the imbalance ratio. Isotonic Regression has better overall performance on imbalanced datasets than parametric and other complex non-parametric methods. However, it performs unstably in highly imbalanced scenarios. This study provides some insights into calibration methods on imbalanced datasets, and it can be a reference for the future development of calibration methods in class imbalance scenarios

Feature selection and classification of imbalanced datasets. Application to PET images of children with Autistic Spectrum Disorders

Learning with discriminative methods is generally based on minimizing themisclassification of training samples, which may be unsuitable for imbalanceddatasets where the recognition might be biased in favor of the most numerousclass. This problem can be addressed with a generative approach, which typicallyrequires more parameters to be determined leading to reduced performances inhigh dimension. In such situations, dimension reduction becomes a crucial issue.We propose a feature selection / classification algorithm based on generativemethods in order to predict the clinical status of a highly imbalanced datasetmade of PET scans of forty-five low-functioning children with autism spectrumdisorders (ASD) and thirteen non-ASD low-functioning children. ASDs aretypically characterized by impaired social interaction, narrow interests, andrepetitive behaviours, with a high variability in expression and severity. Thenumerous findings revealed by brain imaging studies suggest that ASD isassociated with a complex and distributed pattern of abnormalities that makesthe identification of a shared and common neuroimaging profile a difficult task.In this context, our goal is to identify the rest functional brain imagingabnormalities pattern associated with ASD and to validate its efficiency inindividual classification. The proposed feature selection algorithm detected acharacteristic pattern in the ASD group that included a hypoperfusion in theright Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateralpostcentral area. Our algorithm allowed for a significantly accurate (88\%),sensitive (91\%) and specific (77\%) prediction of clinical category. For thisimbalanced dataset, with only 13 control scans, the proposed generativealgorithm outperformed other state-of-the-art discriminant methods. The highpredictive power of the characteristic pattern, which has been automaticallyidentified on whole brains without any priors, confirms previous findingsconcerning the role of STS in ASD. This work offers exciting possibilities forearly autism detection and/or the evaluation of treatment response in individualpatients