Applying Machine Learning Approaches Research Articles

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil’s case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions.

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data.Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior.Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions.Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.

Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa

AimHIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90–90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection.MethodWe applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retained and trained on the most predictive variables. We used the model to identify people living with HIV and individuals with a higher likelihood of contracting the disease.ResultsApplication of XGBoost algorithm appeared to significantly improve identification of HIV positivity over the other five algorithms by f1 scoring mean of 90% and 92% for males and females respectively. Amongst the eight most predictor features in both sexes were: age, relationship with family head, the highest level of education, highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, and wealth quintile. Model performance using these variables increased significantly compared to having all the variables included. We identified five males and 19 females individuals that would require testing to find one HIV positive individual. We also predicted that 4·14% of males and 10.81% of females are at high risk of infection.ConclusionOur findings provide a potential use of the XGBoost algorithm with socio-behavioural-driven data at substantially identifying HIV predictors and predicting individuals at high risk of infection for targeted screening.

Intertemporal defaulted bond recoveries prediction via machine learning

The recovery rate on defaulted corporate bonds has a time-varying distribution, a topic that has received limited attention in the literature. We apply machine learning approaches for intertemporal analysis of U.S. corporate bonds’ recovery rates. We show that machine learning techniques significantly outperform traditional approaches not only out-of-sample as documented in the literature but also in various out-of-time prediction setups. The newly applied sparse power expectation propagation approach provides the most compelling out-of-time prediction results. Motivated by the association of systematic factors with the time-varying characteristic of recovery rates, we study the effect of text-based news measures to account for bond investors’ expectations about the future which translate into market-based recovery rates. Especially during recessions, government-related news are associated with higher recovery rates. Although machine learning is a data-driven approach rather than considering economic intuition for ranking a group of predictors, the most informative groups of predictors for recovery rate prediction are nevertheless economically meaningful.

Applying machine learning approach to predict students’ performance in higher educational institutions

PurposeAcademic success and failure are relevant lifelines for economic success in the knowledge-based economy. The purpose of this paper is to predict the propensity of students’ academic performance using early detection indicators (i.e. age, gender, high school exam scores, region, CGPA) to allow for timely and efficient remediation.Design/methodology/approachA machine learning approach was used to develop a model based on secondary data obtained from students’ information system in a Nigerian university.FindingsResults revealed that age is not a predictor for academic success (high CGPA); female students are 1.2 times more likely to have high CGPA compared to their male counterparts; students with high JAMB scores are more likely to achieve academic success, high CGPA and vice versa; students from affluent and developed regions are more likely to achieve academic success, high CGPA and vice versa; and students in Years 3 and 4 are more likely to achieve academic success, high CGPA.Originality/valueThis predictive model serves as a classifier and useful strategy to mitigate failure, promote success and better manage resources in tertiary institutions.

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95—0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88—0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients’ neural activity.

Improving the clinical interpretation of missense variants in X linked genes using structural analysis

BackgroundImproving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that...

Applying machine learning approach in recycling

Waste generation has been increasing drastically based on the world’s population and economic growth. This has significantly affected human health, natural life, and ecology. The utilization of limited natural resources, and the harming of the earth in the process of mineral extraction, and waste management have far exceeded limits. The recycling rate are continuously increasing; however, assessments show that humans will be creating more waste than ever before. Some difficulties during recycling include the significant expense involved during the separation of recyclable waste from non-disposable waste. Machine learning is the utilization of artificial intelligence (AI) that provides a framework to take as a structural improvement of the fact without being programmed. Machine learning concentrates on the advancement of programs that can obtain the information and use it to learn to make future decisions. The classification and separation of materials in a mixed recycling application in machine learning is a division of AI that is playing an important role for better separation of complex waste. The primary purpose of this study is to analyze AI by focusing on machine learning algorithms used in recycling systems. This study is a compilation of the most recent developments in machine learning used in recycling industries.

A conceptual vision for a bio-intelligent manufacturing cell for Selective Laser Melting

State of the art additive manufacturing (AM) technologies such as Selective Laser Melting (SLM) are traditionally uncontrolled processes, as typically pre-set processing parameters like laser power, scan strategy and speed and many others are used for production. Additionally, the process is influenced by a large number of parameters which today are only covered by statistics. By this such manufacturing systems are not able to actively react to changing conditions as biological systems can do: Higher biological systems are characterized by the availability of a variety of sensor data coming from partly redundant and failure-tolerant sensor systems and intelligence that allow for continuous adaptation and learning. Furthermore, biological systems self-maintain their health and exchange information with other systems to improve their effectiveness. Hence, the adaptation of basic concepts of biological systems to technical systems, and in this case to AM-technologies, seems to be highly beneficial in order to overcome current shortcomings. The paper presents a visionary concept of an SLM system integrating functionalities and behaviors of the operator, and embeds it into a conceptual framework for an intelligent SLM process chain that is capable of self-optimization. It gives an overview on different monitoring technologies and tools of artificial intelligence that need to be used for this concept, and highlights possibilities to apply machine learning approaches in different time scales along the SLM process chain. A rollout to other manufacturing systems can easily be derived from this concept.

An Empirical Review of Automated Machine Learning

In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML.

Technical Analysis in the Stock Market: A Review

Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.

A machine learning based sensitivity analysis of the GTN damage parameters for dynamic fracture propagation in X70 pipeline steel

The Gurson–Tvergaard–Needleman (GTN) damage model is widely used to predict ductile failure initiation and propagation. However, the material-dependent parameters can show a significant spread when determined for the same steel grade material. Different calibration procedures and optimisation processes cause a significant variation in the obtained parameter values. Furthermore, there is no clear consensus on which parameters require calibration. In this study, the influence of the material-dependent parameters used to model the dynamic ductile fracture behaviour of X70 grade pipeline steel is investigated. A sensitivity analysis is performed on a finite element model of a Charpy V-Notch (CVN) specimen. Seven GTN model parameters are considered in a total of 70 simulations. A feedforward back-propagating artificial neural network (ANN) is constructed and trained using data obtained through the numerical simulations. A connected weights (CW) algorithm allows to determine the relative influence of each parameter on the fracture energy. It was observed that the void growth acceleration factor plays an important role with respect to the parameter influences. Remarkably, the mean nucleation strain, \( \varepsilon_{N} \) has the highest relative importance whilst the critical void volume fraction, \( f_{c} \)—which is considered as a crucial damage parameter—showed the smallest influence when the acceleration factor is low. On the contrary, when considering a high acceleration factor, \( f_{c} \) becomes the most influential parameter. Based on the obtained importance for each parameter, it is suggested that parameters \( f_{0} \), \( f_{c} \), \( f_{F} \), and \( f_{N} \) should be selected for calibration in each individual application. Finally, the applied machine learning approach is used to predict the fracture energy for a given set of damage parameters for X70 grade steel. It is observed that the trained neural network is able to provide a satisfactory approximation of the CVN fracture energy.

Global challenges in cognitive assessment: How technology will drive neuropsychologists to the forefront

AbstractBackgroundAge remains the strongest risk factor for Alzheimer’s disease (AD). With the number of people over 65 now outnumbering those under the age of 5, the global burden of AD is rapidly increasing. Cognitive decline is the hallmark behavioral symptom of AD. However, accurate cognitive assessment requires consideration of a myriad of factors, including education, language, and culture. In fact, in a majority of low‐to‐middle income countries, as well as many emerging economies, there is no formalized doctoral training in neuropsychology. These circumstances necessitate embracing novel data collection methods in order to preserve the voice of the neuropsychologist. Technological advances can provide the tools to address the global realities of AD and other related disorders.MethodWe have developed a brain health monitoring platform that leverages the relatively ubiquitous use of smartphones across virtually all countries and collects behavioral measures that are inextricably linked to cognitive skills. We are applying machine learning approaches to quantify digital indices into cognitive ones. Importantly, we have strategically selected mobile applications that measure behaviors that are 1) common regardless of age, education, language and culture and 2) can be flexibly customized to circumstances.ResultWe have begun deploying versions of this high tech‐driven platform in the U.S. Further, we are starting to have an impact internationally, particularly in Asia. The health and age demographics in China make its population particularly vulnerable to AD. We present findings that speak to this risk and how our platform, and others like it, will help fill the neuropsychological expertise gap that such countries are currently experiencing.ConclusionAD is a rising epidemic. Early and accurate detection is the first step to finding disease modifying interventions and providing the context necessary for maximized efficacy. The coupling of professional neuropsychology skills and digital technology will generate solutions that can sufficiently address the impending challenge of conducting cognitive assessments universally. This potentially includes resolving longstanding health disparities by extending neuropsychologists’ reach into regions with significant economic and/or geographical constraints.

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling.

Simple SummaryPersonalized cancer treatments show decreased side-effects and improved treatment success. One aspect of individualized treatment is the timing of medicine intake, which may be optimized based on the biological diurnal rhythm of the patient. The personal biological time can be assessed by a variety of tools not yet commonly included in diagnostics. We review these tools with a focus on their applicability in a clinical context. Using biological samples from the patient, most tools predict individual time using machine learning methodologies, often supported by rhythmicity analysis and mathematical core-clock models. We compare different approaches and discuss possible promising future directions.Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient’s internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient’s internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

COVID-19 detection in radiological text reports integrating entity recognition

COVID-19 diagnosis is usually based on PCR test using radiological images, mainly chest Computed Tomography (CT) for the assessment of lung involvement by COVID-19. However, textual radiological reports also contain relevant information for determining the likelihood of presenting radiological signs of COVID-19 involving lungs. The development of COVID-19 automatic detection systems based on Natural Language Processing (NLP) techniques could provide a great help in supporting clinicians and detecting COVID-19 related disorders within radiological reports. In this paper we propose a text classification system based on the integration of different information sources. The system can be used to automatically predict whether or not a patient has radiological findings consistent with COVID-19 on the basis of radiological reports of chest CT. To carry out our experiments we use 295 radiological reports from chest CT studies provided by the ‘‘HT médica" clinic. All of them are radiological requests with suspicions of chest involvement by COVID-19. In order to train our text classification system we apply Machine Learning approaches and Named Entity Recognition. The system takes two sources of information as input: the text of the radiological report and COVID-19 related disorders extracted from SNOMED-CT. The best system is trained using SVM and the baseline results achieve 85% accuracy predicting lung involvement by COVID-19, which already offers competitive values that are difficult to overcome. Moreover, we apply mutual information in order to integrate the best quality information extracted from SNOMED-CT. In this way, we achieve around 90% accuracy improving the baseline results by 5 points.

Minority Disk Failure Prediction Based on Transfer Learning in Large Data Centers of Heterogeneous Disk Systems

The storage system in large scale data centers is typically built upon thousands or even millions of disks, where disk failures constantly happen. A disk failure could lead to serious data loss and thus system unavailability or even catastrophic consequences if the lost data cannot be recovered. While replication and erasure coding techniques have been widely deployed to guarantee storage availability and reliability, disk failure prediction is gaining popularity as it has the potential to prevent disk failures from occurring in the first place. Recent trends have turned toward applying machine learning approaches based on disk SMART attributes for disk failure predictions. However, traditional machine learning (ML) approaches require a large set of training data in order to deliver good predictive performance. In large-scale storage systems, new disks enter gradually to augment the storage capacity or to replace failed disks, leading storage systems to consist of small amounts of new disks from different vendors and/or different models from the same vendor as time goes on. We refer to this relatively small amount of disks as minority disks. Due to the lack of sufficient training data, traditional ML approaches fail to deliver satisfactory predictive performance in evolving storage systems which consist of heterogeneous minority disks. To address this challenge and improve the predictive performance for minority disks in large data centers, we propose a minority disk failure prediction model named TLDFP based on a transfer learning approach. Our evaluation results in two realistic datasets have demonstrated that TLDFP can deliver much more precise results and lower additional maintenance cost, compared to four popular prediction models based on traditional ML algorithms and two state-of-the-art transfer learning methods.

Abstract A03: Applying machine learning approaches to social environmental data from the U.S. Census in cancer studies: Challenges and considerations

Abstract Background: The precision medicine initiative calls for the study of genes, behaviors, and environment to improve disease prevention. There is a growing body of research supporting the role of social environment (i.e., the neighborhood in which one lives) in cancer health disparities. However, recent efforts have focused on applying empiric, high-dimensional computing approaches to genetic data, with less of an emphasis on environment. In this study, we adapted and applied empiric machine learning approaches to identify which method would be most effective at evaluating the effects of social environment on advanced prostate cancer in a simulated dataset. As is common in high-dimensional data, we encountered (and will present) statistical challenges that arose during analysis, specifically related to multicollinearity. Methods: Pennsylvania Prostate Cancer Registry data from 1995-2005 were linked to publicly available social environmental data from the 2000 U.S. Census via a geocode at the census tract level using ArcGIS software. This primary data consisted of 86,629 prostate cancer cases and 14,663 census variables. U.S. Census variables, which are defined in terms of neighborhood socioeconomic variables, such as education, income, employment, etc., are known to be highly correlated. A simulated dataset was created using the data structure of our primary dataset, where a set of 10 prespecified variables were independent predictors of a binary outcome, and the remaining 990 variables had no effect. Test and training sets were created and various machine learning approaches were applied and compared: standard regression models (REG), Lasso penalized regression (LASSO), elastic net regression (ELNET), and random forest (RF). The most successful method at identifying “true” variables (or highly correlated surrogates), limiting false-positive results, and consistently replicating findings was considered the most effective approach. Simulations were repeated 500 times, and results summarized. Results: Over the 500 simulations, the methods identified 6.3 (REG), 6.4 (LASSO), 8.2 (ELNET), and 10 (RF) of the 10 true (or highly correlated surrogate) variables. In addition, 38.8 (REG), 13.3 (LASSO), 49.9 (ELNET), and 65 (RF) false positive variables were identified. RF consistently replicated the selection of all 10 variables across simulations 100% of the time, whereas LASSO was consistently unable to identify 2 of the 10 true variables. Conclusions: Preliminary findings suggest a combination of RF and LASSO may be the most effective approach; LASSO has the best overall ability to identify true variables while avoiding false positives; RF identifies true variables consistently. Given that Lasso was unable to detect 2 of the true variables, we will also present findings from multivariate models to allow for adjustment due to residual confounding. Final results should be tested in a real data setting where additional considerations for multicollinearity would need to be explored. Citation Format: Shannon. M Lynch, Yinuo Yin, Elizabeth Handorf. Applying machine learning approaches to social environmental data from the U.S. Census in cancer studies: Challenges and considerations [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A03.

The Challenges of Leveraging Threat Intelligence to Stop Data Breaches

Data breaches and security incidents are becoming increasingly costly and statistics show that hackers are highly motivated to acquire confidential data as the financial benefits are substantial. Hence, business data has become a top priority to be compromised. Threat Intelligence has been recently introduced by organisations as a means to gain greater visibility of cyber threats, especially data breaches, in order to better protect their digital assets. A well-planned implementation of threat intelligence enables organisations to predict and (at least partially) prevent cyber crime, such as data breaches or data exfilteration ({\ie} attempts to move data outside an organization’s secure perimeters). This allows an organisation to better understand different aspects of threats, including identifying the adversary and how and why they intend to compromise digital assets, consequences of attacks, which assets can be compromised, to what level and how to detect threats, how to respond to them. A key enabler to implement threat intelligence is to build sophisticated data-driven architectures using machine learning that allows an organisation's cyber data (stored in different silos throughout an organisation's digital infrastructure) to be managed effectively. However, one of the biggest challenges of machine learning in cybersecurity is to enable an efficient implementation that scales in today's complex threat landscapes and digital infrastructure, respectively. In this paper, we review the data breaches problem and discuss the challenges of implementing machine learning to mitigate security threats and data intelligence to predict cyber threats that could potentially lead to data breaches leakage. Then illustrate how the future of effective threat intelligence is closely linked to efficiently applying machine learning approaches in this field, and outline future research directions in this area