Abstract
This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighting the features, (2) performing small perturbations on numeric labels and (3) selecting the features that are relevant in the trained multi-target prediction models. With the purpose of decreasing the computational cost in the SOFIA method, we consider those multi-objective optimization metaheuristics that support parallelization. In this sense, we propose an extension of the Natural Optimization (NO) approach for Simulated Annealing to support a multi-objective (MO) optimization. This proposed extension, called MONO, and some multiobjective evolutionary algorithms (MOEAs) are considered when performing the SOFIA method to improve prediction models in a multi-stage migraine treatment. This work also considers the adaptation of these metaheuristics to run on GPUs for accelerating the exploration of a larger space of solutions and improving results at the same time. The obtained results show that accuracies close to 88% are obtained with the MONO metaheuristic when employing eight threads and when running on a GPU. In addition, training times have been decreased from more than 8 h to less than 45 min when running the algorithms on a GPU. Besides, classification models trained with the SOFIA method only require 15 medical features or fewer to predict treatment responses. All in all, the methods proposed in this work remarkably improve the accuracy of multi-target prediction models for the OnabotulinumtoxinA (BoNT-A) treatment, while selecting those relevant features that allow us to know in advance the response to every stage of the treatment.
Highlights
Nowadays, the medical field has benefited from taking advantage of new technologies through the use of electronic medical records (EMRs)
Due to the economic cost of continuous treatments such as those of hepatitis C [3,4] or migraine [5], it is important for our methodology to be able to know in advance whether a treatment will be effective in patients with
The feature weighting vector will reflect the relevance of every feature to the predictive model and it will be the solution to be found by multi-objective metaheuristic methods
Summary
The medical field has benefited from taking advantage of new technologies through the use of electronic medical records (EMRs). In other works [2,9], the authors propose a data transformation for carrying out the selection of relevant features while achieving high percentages of accuracy when predicting treatment responses in migraine. It will be possible to select those relevant medical features considering all the responses to be predicted while finding that data transformation that allows to obtain high accuracies when performing a multi-target prediction Another aspect to solve is the heterogeneity of clinical data. The proposed SOFIA methodology is applied in a realistic scenario when analyzing EMRs of migraineurs under the OnabotulinumtoxinA (BoNT-A) treatment In this way, the multi-target prediction models have benefited significantly when performing the SOFIA method, achieving mean accuracies close to 88%.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
