Twenty Years of Random Forest: preliminary results of a systematic literature review

The Random Forest (RF) model consists of an ensemble classifier that produces many decision trees through the use of a randomly selected subset of samples and training variables. The RF model has assumed importance within the scientific community thanks to its performance. The accuracy of its classifications and prediction has allowed the use of RF in several research domains, which have benefited from it. The present study aims to provide a preliminary review of the whole sci- entific production characterized by all the publications citing the article ”Random Forest” by Breiman, 2001, in the last 20 years (2001-2021)

A comparison among interpretative proposals for Random Forests

The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields. Despite its growing success, there are some limitations and disadvantages: the most significant is the lack of interpretability that does not allow users to understand how particular decisions are made. Our study focus on one of the best performing and most used models in the Machine Learning framework, the Random Forest model. It is known as an efficient model of ensemble learning, as it ensures high predictive precision, flexibility, and immediacy; it is recognized as an intuitive and understandable approach to the construction process, but it is also considered a Black Box model due to the large number of deep decision trees produced within it. The aim of this research is twofold. We present a survey about interpretative proposal for Random Forest and then we perform a machine learning experiment providing a comparison between two methodologies, inTrees, and NodeHarvest, that represent the main approaches in the rule extraction framework. The proposed experiment compares methods performance on six real datasets covering different data characteristics: n. of observations, balanced/unbalanced response, the presence of categorical and numerical predictors. This study contributes to picture a review of the methods and tools proposed for ensemble tree interpretation, and identify, in the class of rule extraction approaches, the best proposal

Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests

The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not allow users to understand how certain decisions are made. This drawback is identified with the term "Black-Box", as well as models that do not allow to interpret the internal work of certain ML techniques, thus discouraging their use. In a highly regulated and risk-averse context such as healthcare, although "trust" is not synonymous with decision and adoption, trusting an ML model is essential for its adoption. Many clinicians and health researchers feel uncomfortable with black box ML models, even if they achieve high degrees of diagnostic or prognostic accuracy. Therefore more and more research is being conducted on the functioning of these models. Our study focuses on the Random Forest (RF) model. It is one of the most performing and used methodologies in the context of ML approaches, in all fields of research from hard sciences to humanities. In the health context and in the evaluation of health policies, their use is limited by the impossibility of obtaining an interpretation of the causal links between predictors and response. This explains why we need to develop new techniques, tools, and approaches for reconstructing the causal relationships and interactions between predictors and response used in a RF model. Our research aims to perform a machine learning experiment on several medical datasets through a comparison between two methodologies, which are inTrees and NodeHarvest. They are the main approaches in the rules extraction framework. The contribution of our study is to identify, among the approaches to rule extraction, the best proposal for suggesting the appropriate choice to decision-makers in the health domain

AI and ML in accounting and finance: A bibliometric review

Artificial intelligence (AI) and machine learning (ML) are two related technologies in accounting and finance studies. This study maps the conceptual structure of AI and ML research with the aim of contributing to a better understanding of this research stream. A bibliometric analysis of 3,836 documents on ai and ML retrieved from the Web of Science database is conducted. The analysis of descriptive performance indicators identifies the main traits of the scientific debate about AI and ML in terms of publications, productive countries and sources. To map the conceptual structure of the dataset, the study performs a thematic evolution. The results highlight the growing academic interest in the research topic, especially in the past few years. The results of this study may provide scholars with a better understanding of AI and ML research in accounting and finance. This paper contributes to the field by providing an examination of the current state of the art of AI e ML research and identifying possible future research directions

AI and ML in accounting and finance: A bibliometric review

Artificial intelligence (AI) and machine learning (ML) are two related technologies in accounting and finance studies. This study maps the conceptual structure of AI and ML research with the aim of contributing to a better understanding of this research stream. A bibliometric analysis of 3,836 documents on ai and ML retrieved from the Web of Science database is conducted. The analysis of descriptive performance indicators identifies the main traits of the scientific debate about AI and ML in terms of publications, productive countries and sources. To map the conceptual structure of the dataset, the study performs a thematic evolution. The results highlight the growing academic interest in the research topic, especially in the past few years. The results of this study may provide scholars with a better understanding of AI and ML research in accounting and finance. This paper contributes to the field by providing an examination of the current state of the art of AI e ML research and identifying possible future research directions

Effect of empagliflozin on brachial artery shear stress and endothelial function in subjects with type 2 diabetes: Results from an exploratory study:

Empagliflozin reduces the risk of cardiovascular mortality in subjects with type 2 diabetes. We demonstrated that empagliflozin increases blood viscosity and carotid shear stress and decreases carotid wall thickness. Shear stress is the force acting on the endothelial surface and modulates arterial function. The current study evaluates the influence of empagliflozin on brachial artery shear stress and endothelial function compared to incretin-based therapy. The study is a nonrandomized, open, prospective cohort study including 35 subjects with type 2 diabetes administered empagliflozin or incretin-based therapy. Shear stress was calculated with a validated formula, and endothelial function was evaluated using the flow-mediated dilation technique. Both treatments resulted in comparable reductions in blood glucose and glycated haemoglobin. Brachial artery shear stress significantly increased exclusively in the empagliflozin group (61 ± 20 vs 68 ± 25 dynes/cm2, p = 0.04), whereas no significant difference was detected in the incretin-based therapy group (60 ± 20 vs 55 ± 12 dynes/cm2, p = not significant). Flow-mediated dilation significantly increased in the empagliflozin group (4.8 ± 4.5% vs 8.5 ± 5.6%, p = 0.03). Again, no change was detected in the incretin-based therapy group (5.1 ± 4.5% vs 4.7 ± 4.7%, p = not significant). The present findings demonstrate the beneficial effect of empagliflozin on shear stress and endothelial function in subjects with type 2 diabetes independent of the hypoglycaemic effect

Markers of insulin resistance and carotid atherosclerosis. A comparison of the homeostasis model assessment and triglyceride glucose index.

Summary Aims The present investigation was designed to test the association between carotid atherosclerosis and two simple markers of insulin resistance, i.e. HOMA-Index and TyG-Index. Materials and methods The study was performed in two different cohorts. In the first cohort, 330 individuals were enrolled. Blood pressure, lipids, glucose, waist and cigarette smoking were evaluated. HOMA-IR and TyG-Index were calculated as markers of prevalent hepatic and muscular insulin resistance respectively. Carotid atherosclerosis was assessed by Doppler ultrasonography. The association between cardiovascular risk factors, markers of insulin resistance and carotid atherosclerosis was assessed by multiple logistic regression analyses. In the second cohort, limited to the evaluation of TyG-Index, 1432 subjects were studied. Results In the first cohort, TyG-Index was significantly associated with carotid atherosclerosis in a model including age, sex, diabetes, cigarette smoking and LDL cholesterol, while HOMA-IR was not. When components of metabolic syndrome were added to the model as dichotomous variables (absent/present), TyG-Index retained its predictive power. The same result was obtained when the metabolic syndrome was added to the model (absence/presence). The association between TyG-Index and carotid atherosclerosis was confirmed in the second cohort. Conclusions The present findings suggest that TyG-Index is better associated with carotid atherosclerosis than HOMA-IR