Stability Selection using a Genetic Algorithm and Logistic Linear Regression on Healthcare Records

This paper presents a Genetic Algorithm (GA) application to measuring feature importance in machine learning (ML) from a large-scale database. Too many input features may cause over-fitting, therefore a feature selection is desirable. Some ML algorithms have feature selection embedded, e.g., lasso penalized linear regression or random forests. Others do not include such functionality and are sensitive to over-fitting, e.g., unregularized linear regression. The latter algorithms require that proper features are chosen before learning. Therefore, we propose a novel stability selection (SS) approach using GA-based feature selection. The proposed SS approach iteratively applies GA on a subsample of records and features. Each GA individual represents a binary vector of selected features in the subsample. An unregularized logistic linear regression model is then trained and tested using GA-selected features through cross-validation of the subsamples. GA fitness is evaluated by area under the curve (AUC) and optimized during a GA run. AUC is assessed with an unregularized logistic regression model on multiple-subsampled healthcare records, collected under the Healthcare Cost, and Utilization Project (HCUP), utilizing the National (Nationwide) Inpatient Sample (NIS) database. Reported results show that averaging feature importance from top-4 SS and the SS using GA (GASS), improves these AUC results

Interpretable Machine Learning Model for Clinical Decision Making

Despite machine learning models being increasingly used in medical decision-making and meeting classification predictive accuracy standards, they remain untrusted black-boxes due to decision-makers\u27 lack of insight into their complex logic. Therefore, it is necessary to develop interpretable machine learning models that will engender trust in the knowledge they generate and contribute to clinical decision-makers intention to adopt them in the field. The goal of this dissertation was to systematically investigate the applicability of interpretable model-agnostic methods to explain predictions of black-box machine learning models for medical decision-making. As proof of concept, this study addressed the problem of predicting the risk of emergency readmissions within 30 days of being discharged for heart failure patients. Using a benchmark data set, supervised classification models of differing complexity were trained to perform the prediction task. More specifically, Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Gradient Boosting Machines (GBM) models were constructed using the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD). The precision, recall, area under the ROC curve for each model were used to measure predictive accuracy. Local Interpretable Model-Agnostic Explanations (LIME) was used to generate explanations from the underlying trained models. LIME explanations were empirically evaluated using explanation stability and local fit (R2). The results demonstrated that local explanations generated by LIME created better estimates for Decision Trees (DT) classifiers

Online Machine Learning Algorithms Review and Comparison in Healthcare

Currently, the healthcare industry uses Big Data for essential patient care information. Electronic Health Records (EHR) store massive data and are continuously updated with information such as laboratory results, medication, and clinical events. There are various methods by which healthcare data is generated and collected, including databases, healthcare websites, mobile applications, wearable technologies, and sensors. The continuous flow of data will improve healthcare service, medical diagnostic research and, ultimately, patient care. Thus, it is important to implement advanced data analysis techniques to obtain more precise prediction results.Machine Learning (ML) has acquired an important place in Big Healthcare Data (BHD). ML has the capability to run predictive analysis, detect patterns or red flags, and connect dots to enhance personalized treatment plans. Because predictive models have dependent and independent variables, ML algorithms perform mathematical calculations to find the best suitable mathematical equations to predict dependent variables using a given set of independent variables. These model performances depend on datasets and response, or dependent, variable types such as binary or multi-class, supervised or unsupervised.The current research analyzed incremental, or streaming or online, algorithm performance with offline or batch learning (these terms are used interchangeably) using performance measures such as accuracy, model complexity, and time consumption. Batch learning algorithms are provided with the specific dataset, which always constrains the size of the dataset depending on memory consumption. In the case of incremental algorithms, data arrive sequentially, which is determined by hyperparameter optimization such as chunk size, tree split, or hoeffding bond. The model complexity of an incremental learning algorithm is based on a number of parameters, which in turn determine memory consumption

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: Applications and challenges

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research

Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning

In biomedical research, many different types of patient data can be collected, such as various types of omics data and medical imaging modalities. Applying multi-view learning to these different sources of information can increase the accuracy of medical classification models compared with single-view procedures. However, collecting biomedical data can be expensive and/or burdening for patients, so that it is important to reduce the amount of required data collection. It is therefore necessary to develop multi-view learning methods which can accurately identify those views that are most important for prediction. In recent years, several biomedical studies have used an approach known as multi-view stacking (MVS), where a model is trained on each view separately and the resulting predictions are combined through stacking. In these studies, MVS has been shown to increase classification accuracy. However, the MVS framework can also be used for selecting a subset of important views. To study the view selection potential of MVS, we develop a special case called stacked penalized logistic regression (StaPLR). Compared with existing view-selection methods, StaPLR can make use of faster optimization algorithms and is easily parallelized. We show that nonnegativity constraints on the parameters of the function which combines the views play an important role in preventing unimportant views from entering the model. We investigate the performance of StaPLR through simulations, and consider two real data examples. We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR is often more conservative and has a consistently lower false positive rate.Comment: 26 pages, 9 figures. Accepted manuscrip

The use of genetic programming for detecting the incorrect predictions of classification models

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsCompanies around the world use Advanced Analytics to support their decision making process. Traditionally they used Statistics and Business Intelligence for that, but as the technology is advancing, the more complex models are gaining popularity. The main reason for an increasing interest in Machine Learning and Deep Learning models is the fact that they reach a high prediction accuracy. On the second hand with good performance, comes an increasing complexity of the programs. Therefore the new area of Predictors was introduced, it is called Explainable AI. The idea is to create models that can be understood by business users or models to explain other predictions. Therefore we propose the study in which we create a separate model, that will serve as a very er for the machine learning models predictions. This work falls into area of Post-processing of models outputs. For this purpose we select Genetic Programming, that was proven to be successful in various applications. In the scope of this research we investigate if GP can evaluate the prediction of other models. This area of applications was not explored yet, therefore in the study we explore the possibility of evolving an individual for another model validation. We focus on classi cation problems and select 4 machine learning models: logistic regression, decision tree, random forest, perceptron and 3 di erent datasets. This set up is used for assuring that during the research we conclude that the presented idea is universal for di erent problems. The performance of 12 Genetic Programming experiments indicates that in some cases it is possible to create a successful model for errors prediction. During the study we discovered that the performance of GP programs is mostly connected to the dataset on the experiment is conducted. The type of predictive models does not in uence the performance of GP. Although we managed to create good classi ers of errors, during the evolution process we faced the problem of over tting. That is common in problems with imbalanced datasets. The results of the study con rms that GP can be used for the new type of problems and successfully predict errors of Machine Learning Models