Decision Trees Research Papers - Academia.edu (original) (raw)

Food and Drug Administration (FDA) has introduced a distinctive regulatory program known as Over-the-Counter Monograph Drug User Fee Program (OMUFA) to improve the efficacy and security of over-the-counter (OTC) medications made available... more

Food and Drug Administration (FDA) has introduced a distinctive regulatory program known as Over-the-Counter Monograph Drug User Fee Program (OMUFA) to improve the efficacy and security of over-the-counter (OTC) medications made available to consumers. The program, which represents a pivotal shift in the regulatory landscape, aims to address the challenges associated with the oversight of OTC monograph drugs. The OMUFA's primary objective is to expedite the review and approval process of OTC monograph drugs while maintaining stringent safety standards. By imposing user fees on manufacturers and sponsors seeking to bring new OTC products to market or seeking updates for existing ones, the program is designed to support the FDA's ability to allocate additional resources for timely reviews and assessments. This work delves into the key components and mechanics of the OMUFA, such as the user fee structure, types of submissions covered, and the corresponding performance goals established for the FDA. While acknowledging the benefits of the OMUFA, this work also discusses potential challenges and concerns raised by industry stakeholders and consumer advocacy groups. This critical regulatory initiative has the potential to facilitate further research and discussions on optimizing drug safety and access within the OTC market through required modifications and initiatives.

Identifying the critical elements that impact students' choice of university majors is critical for leading the nation towards future success. This entails developing a cohort of talented science experts who can pioneer innovations in a... more

Identifying the critical elements that impact students' choice of university majors is critical for leading the nation towards future success. This entails developing a cohort of talented science experts who can pioneer innovations in a variety of industries such as technology, industry, and transportation. To uncover these crucial aspects, this study uses artificial neural networks, a sophisticated analytical technique, to anticipate and assess their relative importance. The study used a descriptive analytical technique, administering a detailed questionnaire to students enrolled in Iraqi universities, resulting in a strong sample size of 238 people. The data was then meticulously analysed using artificial neural networks, a process well-known for its ability to find nuanced patterns and correlations within complicated datasets. The study's findings provide fascinating insights into the factors that influence students' university major selections. The quality of interactions with instructors and classmates during high school is among the most important of these characteristics, demonstrating the substantial influence of mentoring and social dynamics on academic trajectory. Following closely after are thoughts about the economic condition of students' parents, demonstrating the ubiquitous effect of socioeconomic issues on educational decision-making. In summary, this study provides useful information that may be used to guide strategic activities targeted at creating a favourable environment for the development of future leaders. By identifying the fundamental determinants underlying students' university major choices, policymakers and stakeholders may develop focused interventions to assure the nation's preparation for tomorrow's knowledge economy's problems and prospects.

The purpose of this analysis is to present a method to determine the value of diagnostic tests for evidence of bacterial activity related to periodontal disease. Five population studies are discussed: 1) screening the general population;... more

The purpose of this analysis is to present a method to determine the value of diagnostic tests for evidence of bacterial activity related to periodontal disease. Five population studies are discussed: 1) screening the general population; 2) screening the patients of a general practice; 3) diagnosing initial patients referred to a periodontist; 4) monitoring periodontal maintenance patients; and 5) monitoring postsurgical refractory patients. A decision tree is constructed in which the primary decision branches are test and no test. Chance branches after the test branch were test positive and test negative. The treatment alternatives in the treatment branches were prophylaxis and oral hygiene, scaling and root planing, and advanced periodontal therapy (including surgical intervention and/or antibiotics or chemotherapies). Probabilities for the prevalence of disease in each population were obtained from epidemiological surveys. Disease activity and outcome states were estimated from c...

Um conjunto de dados desbalanceado ocorre quando há diferença no número de amostras em diferentes classes. A fase de aprendizagem para a predição do modelo pode ser afetada em caso de dados desbalanceados. Então, neste estudo, foram... more

Um conjunto de dados desbalanceado ocorre quando há diferença no número de amostras em diferentes classes. A fase de aprendizagem para a predição do modelo pode ser afetada em caso de dados desbalanceados. Então, neste estudo, foram aplicadas técnicas de oversampling e undesampling para lidar com dados desbalanceados. Os resultados mostraram um melhor desempenho do modelo Random Forest e das técnicas de oversampling para as métricas acurácia e precisão, um melhor desempenho das técnicas de oversampling para a métrica F1 e um melhor desempenho das técnicas de undersampling para as métricas recall e área soba curva ROC.

Let R and B be sets of red and blue points in the plane in general position. We study the problem of computing a k-level binary space partition (BSP) tree to classify/separate R and B, such that the tree defines a linear decision at each... more

Let R and B be sets of red and blue points in the plane in general position. We study the problem of computing a k-level binary space partition (BSP) tree to classify/separate R and B, such that the tree defines a linear decision at each internal node and each leaf of the tree corresponds to a (convex) cell of the partition that contains only red or only blue points. Specifically, we show that a 2-level tree can be computed, if one exists, in time O(n2). We show that a minimum-level (3 ≤ k ≤ log n) tree can be computed in time nO( log n). In the special case of axis-parallel partitions, we show that 2-level and 3-level trees can be computed in time O(n), while a minimum-level tree can be computed in time O(n5).

The autonomic nervous system (ANS) plays a role as a modulator in the pathogenesis of paroxysmal atrial fibrillation (PAF). The clinical pattern of vagally mediated PAF has been observed mainly in young patients. Neurocardiogenic... more

The autonomic nervous system (ANS) plays a role as a modulator in the pathogenesis of paroxysmal atrial fibrillation (PAF). The clinical pattern of vagally mediated PAF has been observed mainly in young patients. Neurocardiogenic responses during orthostatic stress ...

¿Qué tienen en común organizaciones tales como Blockbuster, PAN AM, KODAK, Black Berry o TERRA? Todas y cada una de ellas fueron referentes que marcaron época en sus respectivas industrias, y hoy no son más que recuerdos para algunas... more

¿Qué tienen en común organizaciones tales como Blockbuster, PAN AM, KODAK, Black Berry o TERRA? Todas y cada una de ellas fueron referentes que marcaron época en sus respectivas industrias, y hoy no son más que recuerdos para algunas generaciones. La mayor parte de ellas se encuentra completamente extintas. Así ocurre con algunos de los viejos paradigmas empresariales tales como “too big to fail” (muy grande para caer) los que simplemente ya no tienen vigencia en el mundo de la cuarta revolución industrial.

Sketch is a type of natural, simple and convenient human-computer interaction which conveys ambiguous conceptions and imperfect information. It adapts to creative activities and improves the collaboration and communication among people.... more

Sketch is a type of natural, simple and convenient human-computer interaction which conveys ambiguous conceptions and imperfect information. It adapts to creative activities and improves the collaboration and communication among people. With the rapidly extended application of hand-written characters and hand-drawn sketches, online sketchy symbol recognition has been widely studied, in the process of which symbol feature representation, intelligentized recognition approaches, learning ability and the extension ...

Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. The study... more

Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. The result...

Background: To assess the clinicopathological outcome of women with adnexal masses . Method: In this observational study patients with a diagnosis of adnexal mass, who underwent laparotomy, were included. All the patients were evaluated... more

Background: To assess the clinicopathological outcome of women with adnexal masses . Method: In this observational study patients with a diagnosis of adnexal mass, who underwent laparotomy, were included. All the patients were evaluated by a complete history, general abdominal and pelvic examination, followed by ultrasonography. Their preoperative findings are then correlated with surgical findings and histopathological diagnosis. Descriptive statistics are applied and results shown in the form of frequencies and percentages. Result: In 50 patients commonest presenting symptom was pain abdomen followed by mass abdomen. Ultrasound features correlate well with histopathological features. Majority (94.6%) patients had benign adnexal pathology and 5.4% had malignant pathology. Most common neoplasms were surface epithelial tumours followed by dermoid cyst. Conclusion:Demographic detail, ultrasonography and CA-125 are good preoperative indicators of malignant nature or benign nature of ad...

The aim of this research is to analyze: (1) The influence of motivation on the performance of PPL, (2) The influence of work discipline on the performance of PPL, (3) The influence of transformational leadership on the performance of PPL.... more

The aim of this research is to analyze: (1) The influence of motivation on the performance of PPL, (2) The influence of work discipline on the performance of PPL, (3) The influence of transformational leadership on the performance of PPL. This research tests the exogenous influence of endogenous variables. Hypothesis testing uses linear regression analysis with SPSS 26. 81 respondents were sampled in this study. The results show: (1) Motivation has a significant positive effect on performance; (2) Discipline has a significant positive effect on performance; and (3) Transformational Leadership has a significant positive effect on performance. It is recommended that future researchers further explore other variables that are thought to influence performance.

Given the financial crises in the world, one of the most important issues of banking industry is the assessment of customers' credit to distinguish bad credit customers from good credit customers. The problem of customer credit risk... more

Given the financial crises in the world, one of the most important issues of banking industry is the assessment of customers' credit to distinguish bad credit customers from good credit customers. The problem of customer credit risk assessment is a binary classification problem, which suffers from the lack of data and sophisticated features as main challenges. In this paper, an adaptive neuro-fuzzy inference system is exploited to tackle the customer credit risk assessment problem regarding the mentioned challenges. First of all, a SOMTE-based algorithm is introduced to overcome the data imbalancing problem. Then, several efficient features are identified using a MEMETIC metaheuristic algorithm, and finally an adaptive neuro-fuzzy system is exploited for distinguishing bad credit customers from good ones. To evaluate and compare the performance of the proposed system, the standard German credit data dataset and the well-known classification algorithms are utilized. The results indicate the superiority of the proposed system compared to some well-known algorithms in terms of precision, accuracy, and Type II errors.

Metabolic Syndrome (MS) is the collection of risk factors for coronary artery disease (CAD). It is responsible for cardiovascular disease (CVD), type 2 diabetes, cancer, renal and mental diseases with the transition from childhood to... more

Metabolic Syndrome (MS) is the collection of risk factors for coronary artery disease (CAD). It is responsible for cardiovascular disease (CVD), type 2 diabetes, cancer, renal and mental diseases with the transition from childhood to adulthood. The MS is increasing in recent decades in different societies, especially in Iran. This study was conducted to determine the predictive factors of MS in children and adolescents in Birjand (Capital of South Khorasan Province, Iran) using a data mining approach. The four different analyses for females and males (6-11 and 12-18 year-old groups) were carried out using three popular decision tree models. The most important prognostic factors for MS were high level of TG and low level of DBP and HDL in 6-11-year-old group, and high level of waist circumference (WC) and low level of TG for 12-18-year-old group. The most important factors were TG and HDL in females and WC and TG in males. Raising teens and families' awareness of the risk factors, screening children and teens, monitoring and controlling the risk factors through life style correction such as more physical activity and healthy eating are recommended.

Purpose Most patients with melanoma have microscopically thin (≤ 1 mm) primary lesions and are cured with excision. However, some develop metastatic disease that is often fatal. We evaluated established prognostic factors to develop... more

Purpose Most patients with melanoma have microscopically thin (≤ 1 mm) primary lesions and are cured with excision. However, some develop metastatic disease that is often fatal. We evaluated established prognostic factors to develop classification schemes with better discrimination than current American Joint Committee on Cancer (AJCC) staging. Patients and Methods We studied patients with thin melanomas from the US population-based Surveillance, Epidemiology, and End Results (SEER) cancer registry (1988 to 2001; n = 26,291) and those seen by the University of Pennsylvania's Pigmented Lesion Group (PLG; 1972 to 2001; n = 2,389; Philadelphia, PA). AJCC prognostic factors were thickness, anatomic level, ulceration, site, sex, and age; PLG prognostic factors also included a set of biologically based candidate prognostic factors. Recursive partitioning was used to develop a SEER-based classification tree that was validated using PLG data. Next, a new PLG-based classification tree wa...

PurposeThe majority of invasive primary melanomas are thin (≤ 1.00 mm). Since the current staging system imperfectly predicts outcome in patients with such lesions, we sought to develop a more effective classification scheme to better... more

PurposeThe majority of invasive primary melanomas are thin (≤ 1.00 mm). Since the current staging system imperfectly predicts outcome in patients with such lesions, we sought to develop a more effective classification scheme to better identify both patients at high risk of metastasis who are candidates for further staging and therapy and those with little risk.Patients and MethodsThis prospective cohort study included 884 patients who had thin invasive melanomas. A tree-structured analysis of 10-year metastasis was used to develop a new classification scheme.ResultsThe overall 10-year metastasis rate was 6.5% (95% CI, 4.8% to 8.1%). The prognostic tree defined four risk groups: high-risk: men with vertical growth phase (VGP) lesions that had mitotic rates (MRs) greater than 0, and for whom the 10-year metastasis rate was 31% (22% to 42%; n = 90); moderate-risk: women with VGP lesions that had MRs greater than 0 and for whom the rate was 13% (9% to 18%; n = 136); low-risk: patients w...

The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine... more

The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF ...

The drive to improve the academic performance of students at an open and distance learning (ODL) institution has resulted in the incorporation of a blended learning component, namely satellite classes, in the learning strategy to enhance... more

The drive to improve the academic performance of students at an open and distance learning (ODL) institution has resulted in the incorporation of a blended learning component, namely satellite classes, in the learning strategy to enhance the academic performance of first year diploma students in Business Management and Management. Monitoring this intervention to justify implementation costs (Mathur & Oliver, 2007:3) and effectiveness in relation to student performance is essential. Whereas an initial study confirmed a statistically significant relationship between satellite class attendance and academic performance, this study evaluated the interaction effect of satellite classes and additional, potential success predictors on academic performance by applying the Chi-square Automatic Interaction Detector (CHAID) methodology. This decision tree methodology described the interactive driving forces that impacted on student success. Satellite class intervention and biographical student ...

The replacement of old radiologic contrast media with supposedly safer but more expensive media has created a dilemma for radiologists and hospital administrators. To quantitate the nature of this trade-off we performed a cost-utility... more

The replacement of old radiologic contrast media with supposedly safer but more expensive media has created a dilemma for radiologists and hospital administrators. To quantitate the nature of this trade-off we performed a cost-utility analysis using optimistic assumptions that favoured the new media. A complete conversion to the new media would result in an incremental cost of at least 65,000togain1quality−adjustedlife−year(QALY).Foraselectivestrategyinwhichonlyhigh−riskpatientswouldreceivethenewmediathecostwouldbeabout65,000 to gain 1 quality-adjusted life-year (QALY). For a selective strategy in which only high-risk patients would receive the new media the cost would be about 65,000togain1quality−adjustedlife−year(QALY).Foraselectivestrategyinwhichonlyhigh−riskpatientswouldreceivethenewmediathecostwouldbeabout23,000 per QALY gained. However, the incremental cost for low-risk patients is over $220,000 per QALY gained. Conversion to the new contrast media, although not necessarily the most efficient use of scarce resources, has already occurred in Ontario, primarily because of press publicity, pressure from insurers and a political unwillingness of policymakers to decide the fate of identifiable victims. We found that funding of a new intervention associated with a high cost-utility ratio rather than interventions with lower ratios might save some identifiable victims at the expense of a larger number of unidentifiable ones.

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become... more

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.

INTRODUCTION An ideal drug delivery system should be able to deliver an adequate amount of drug, preferably for an extended period of time for its optimum therapeutic activity. Most drugs are inherently not long lasting in the body and... more

INTRODUCTION An ideal drug delivery system should be able to deliver an adequate amount of drug, preferably for an extended period of time for its optimum therapeutic activity. Most drugs are inherently not long lasting in the body and require multiple daily dosing to achieve the ...