This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements

Biomarkers have already been proposed as powerful classification features for use in the training of neural network-based and other machine learning and artificial intelligence-based prognostic models in the scientific field of personalized nutrition. In this paper, we construct and study cascaded SVM-based classifiers for automated metabolic syndrome diagnosis. Specifically, using blood exams, we achieve an average accuracy of about 84% in correctly classifying body mass index. Similarly, cascaded SVM-based classifiers achieve a 74% accuracy in correctly classifying systolic blood pressure. Next, we propose and implement a system that achieves an 84% accuracy in metabolic syndrome prediction. The proposed system relies not only on prediction of the body mass index but also on prediction from blood exams of total cholesterol, triglycerides and glucose. For the aim of self-completeness of the paper, the key concepts with regard to metabolic syndrome are summarized, and a review of previous related work is included. Finally, conclusions are drawn and indications for related future research are outlined.

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.

Biomarkers are measurements of biological variables that can determine a state of health. They consist of measuring a single variable or a combination of variables related to the state of health that these variables represent. Biomarkers can provide an early warning of a health problem in relation to an individual patient or group of patients, and thus trigger actions and lead to interventions. Nutritional biomarkers measure the biological consequences of one’s diet. In our recent work, we have used machine learning to predict weight, metabolic syndrome and blood pressure, using blood-exam-based biomarkers. In the current work, we use extreme value theory to examine the significance of outliers in health data, with a focus on diet and the standard biochemistry profile. Specifically, we show that, using extreme value analysis and by applying a systemisation of the process, health trends can be predicted, and thus, health interventions can be (at least partially) automated. For that purpose, public access datasets have been used, which were retrieved from the National Health and Nutrition Examination Survey. The NHANES is a program of studies designed to assess the health and nutritional status of the population in the United States. In total, about 70,000 datapoints were analysed, covering about a decade’s worth of observations.