Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

A New Scalable, Portable, and Memory-Efficient Predictive Analytics Framework for Predicting Time-to-Event Outcomes in Healthcare

Time-to-event outcomes are prevalent in medical research. To handle these outcomes, as well as censored observations, statistical and survival regression methods are widely used based on the assumptions of linear association; however, clinicopathological features often exhibit nonlinear correlations. Machine learning (ML) algorithms have been recently adapted to effectively handle nonlinear correlations. One drawback of ML models is that they can model idiosyncratic features of a training dataset. Due to this overlearning, ML models perform well on the training data but are not so striking on test data. The features that we choose indirectly influence the performance of ML prediction models. With the expansion of big data in biomedical informatics, appropriate feature engineering and feature selection are vital to ML success. Also, an ensemble learning algorithm helps decrease bias and variance by combining the predictions of multiple models. In this study, we newly constructed a scalable, portable, and memory-efficient predictive analytics framework, fitting four components (feature engineering, survival analysis, feature selection, and ensemble learning) together. Our framework first employs feature engineering techniques, such as binarization, discretization, transformation, and normalization on raw dataset. The normalized feature set was applied to the Cox survival regression that produces highly correlated features relevant to the outcome.The resultant feature set was deployed to “eXtreme gradient boosting ensemble learning” (XGBoost) and Recursive Feature Elimination algorithms. XGBoost uses a gradient boosting decision tree algorithm in which new models are created sequentially that predict the residuals of prior models, which are then added together to make the final prediction. In our experiments, we analyzed a cohort of cardiac surgery patients drawn from a multi-hospital academic health system. The model evaluated 72 perioperative variables that impact an event of readmission within 30 days of discharge, derived 48 significant features, and demonstrated optimum predictive ability with feature sets ranging from 16 to 24. The area under the receiver operating characteristics observed for the feature set of 16 were 0.8816, and 0.9307 at the 35th, and 151st iteration respectively. Our model showed improved performance compared to state-of-the-art models and could be more useful for decision support in clinical settings

Data Mining Application for Healthcare Sector: Predictive Analysis of Heart Attacks

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceCardiovascular diseases are the main cause of the number of deaths in the world, being the heart disease the most killing one affecting more than 75% of individuals living in countries of low and middle earnings. Considering all the consequences, firstly for the individual’s health, but also for the health system and the cost of healthcare (for instance, treatments and medication), specifically for cardiovascular diseases treatment, it has become extremely important the provision of quality services by making use of preventive medicine, whose focus is identifying the disease risk, and then, applying the right action in case of early signs. Therefore, by resorting to DM (Data Mining) and its techniques, there is the ability to uncover patterns and relationships amongst the objects in healthcare data, giving the potential to use it more efficiently, and to produce business intelligence and extract knowledge that will be crucial for future answers about possible diseases and treatments on patients. Nowadays, the concept of DM is already applied in medical information systems for clinical purposes such as diagnosis and treatments, that by making use of predictive models can diagnose some group of diseases, in this case, heart attacks. The focus of this project consists on applying machine learning techniques to develop a predictive model based on a real dataset, in order to detect through the analysis of patient’s data whether a person can have a heart attack or not. At the end, the best model is found by comparing the different algorithms used and assessing its results, and then, selecting the one with the best measures. The correct identification of early cardiovascular problems signs through the analysis of patient data can lead to the possible prevention of heart attacks, to the consequent reduction of complications and secondary effects that the disease may bring, and most importantly, to the decrease on the number of deaths in the future. Making use of Data Mining and analytics in healthcare will allow the analysis of high volumes of data, the development of new predictive models, and the understanding of the factors and variables that have the most influence and contribution for this disease, which people should pay attention. Hence, this practical approach is an example of how predictive analytics can have an important impact in the healthcare sector: through the collection of patient’s data, models learn from it so that in the future they can predict new unknown cases of heart attacks with better accuracies. In this way, it contributes to the creation of new models, to the tracking of patient’s health data, to the improvement of medical decisions, to efficient and faster responses, and to the wellbeing of the population that can be improved if diseases like this can be predicted and avoided. To conclude, this project aims to present and show how Data Mining techniques are applied in healthcare and medicine, and how they contribute for the better knowledge of cardiovascular diseases and for the support of important decisions that will influence the patient’s quality of life