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An effective subject recognition approach is designed in this paper, using ground reaction force (GRF) measurements of human gait. The method is a three-stage procedure: 1) The original GRF data are translated through wavelet packet (WP) transform in the time-frequency domain. Using a fuzzy-set-based criterion, we determine an optimal WP decomposition, involving feature subspaces with distinguishing gait characteristics. 2) A feature extraction scheme is employed next for wavelet feature ranking, according to discrimination power. 3) The classification task is accomplished by means of a kernel-based support vector machine. The design parameters of the classifier are tuned through a genetic algorithm to improve recognition rates. The method is evaluated on a database comprising GRF records obtained from 40 subjects. To account for the natural variability of human gait, the experimental setup is designed, allowing different walking speeds and loading conditions. Simulation results demonstrate that high recognition rates can be achieved with moderate number of features and for different training/testing settings. Finally, the performance of our approach is favorably compared with the one obtained using other traditional classification algorithms.

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We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT). Methods and results:We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre-transplant variables. The study cohort comprised 18 625 patients (53 ± 13 years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre-transplant variables, 39 were selected as highly predictive of 1-year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1-year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K-nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model-agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors.ML models for 3-and 5-year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively. Conclusion:Machine learning models showed good predictive accuracy of outcomes after heart transplantation.

Osteoarthritis is the most common form of arthritis in the knee that comes with a variation in symptoms' intensity, frequency and pattern. Knee OA (KOA) is often diagnosed using invasive and expensive methods that can measure changes in joint morphology and function. Early and accurate identification of significant risk factors in clinical data is of vital importance in diagnosing KOA. A machine intelligence approach is proposed here to enable automated, non-invasive identification of risk factors from self-reported clinical data about joint symptoms, disability, function and general health. The proposed methodology was applied to recognize participants with symptomatic KOA or being at high risk of developing KOA in at least one knee. Different machine learning and deep learning algorithms were tested and compared in terms of multiple criteria e.g. accuracy, per class accuracy and execution time. Deep learning was proved to be the most effective in terms of accuracy with classification accuracies up to 86.95%, evaluated on data from the osteoarthritis initiative study. Insights about ten different feature subsets and their effect on classification accuracy are provided. The proposed methodology was also demonstrated in subgroups defined by gender and age. The results suggest that machine intelligence and especially deep learning may facilitate clinical evaluation, monitoring and even prediction of knee osteoarthritis. Apart from the classical implementation of the proposed methodology, a quantum perspective is also discussed highlighting the future application of quantum computers in OA diagnosis.