An Evolutionary Misclassification Cost Minimization Approach for Medical Diagnosis

2011 ◽  
pp. 32-44 ◽  
Author(s):  
Parag C. Pendharkar ◽  
Sudhir Nanda ◽  
James A. Rodger ◽  
Rahul Bhaskar
Keyword(s):  
Medical Diagnosis ◽  
Cost Minimization ◽  
Real Life ◽  
Classification Systems ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Misclassification Cost ◽  
Misclassification Costs ◽  
Error Costs

This chapter illustrates how a misclassification cost matrix can be incorporated into an evolutionary classification system for medical diagnosis. Most classification systems for medical diagnosis have attempted to minimize the misclassifications (or maximize correctly classified cases). The minimizing misclassification approach assumes that Type I and Type II error costs for misclassification are equal. There is evidence that these costs are not equal and incorporating costs into classification systems can lead to superior outcomes. We use principles of evolution to develop and test a genetic algorithm (GA) based approach that incorporates the asymmetric Type I and Type II error costs. Using simulated and real-life medical data, we show that the proposed approach, incorporating Type I and Type II misclassification costs, results in lower misclassification costs than LDA and GA approaches that do not incorporate these costs.

scholarly journals Application of Categorical Data-nested Design of Knowledge & Control Practices of HBV Infection

2020 ◽  
pp. 21-29
Author(s):  
O. A. P. Otaru ◽  
P. N. Ogbonda
Keyword(s):  
Categorical Data ◽  
Real Life ◽  
Hbv Infection ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Mixed Design ◽  
Observational Errors ◽  
And Control ◽  
Main Factor

In real-life, most experimental data are presented in frequencies with no underlying metric probably because of some reasons such as less susceptibility to observational errors. Unfortunately, some of these data have been erroneously analyzed resulting to either type I or type II error. The significance of main factor (University) and sub-factor (Faculty) are studied using categorical data in nested classification. The CATANOVA technique used is suitable for mixed design, having some factors crossed and others nested. The study considered frequency data involving response scores of student’s knowledge and control practices of HBV infection using a scale of good, fair and poor. Numerical results revealed that the main factor, University and the sub-factor, Faculty are not significant (p>0.05) in each case. More so, there was poor level of student’s knowledge and control practices of HBV infection which were also found to be significantly (p>0.05) same in Universities.

Lead-Free Metal Halide Perovskite Nanocrystals for Photocatalysis in Water

2019 ◽  
Author(s):  
Subhajit Bhattacharjee ◽  
Sonu Pratap Chaudhary ◽  
Sayan Bhattacharyya
Keyword(s):  
Charge Carrier ◽  
Wide Spectrum ◽  
Real Life ◽  
Metal Halide ◽  
Lead Free ◽  
Water Medium ◽  
Type I ◽  
Type Ii ◽  
Perovskite Layer ◽  
Halide Perovskites

<p>Metal halide perovskites with high absorption coefficient, direct generation of free charge carriers, excellent ambipolar charge carrier transport properties, point-defect tolerance, compositional versatility and solution processability are potentially transforming the photovoltaics and optoelectronics industries. However their limited ambient stability, particularly those of iodide perovskites, obscures their use as photocatalysts especially in aqueous medium. In an unprecedented approach we have exploited the photo-absorption property of the less toxic lead-free Cs<sub>3</sub>Bi<sub>2</sub>X<sub>9 </sub>(X = Br, I) nanocrystals (NCs) to catalyse the degradation of water pollutant organic dye, methylene blue (MB) in presence of visible light at room temperature. After providing a proof-of-concept with bromide perovskites in isopropanol, the perovskites are employed as photocatalysts in water medium by designing perovskite/Ag<sub>2</sub>S and perovskite/TiO<sub>2 </sub>composite systems, with Type I (or quasi Type II) and Type II alignments, respectively. Ag<sub>2</sub>S and TiO<sub>2</sub> coatings decelerate penetration of water into the perovskite layer while facilitating charge carrier extraction. With a minimal NC loading, Cs<sub>3</sub>Bi<sub>2</sub>I<sub>9</sub>/Ag<sub>2</sub>S degrades ~90% MB within an hour. Our approach has the potential to unravel the photocatalytic properties of metal halide perovskites for a wide spectrum of real-life applications. </p>

Type I and type II error rates for quantitative trait loci (QTL) mapping studies using recombinant inbred mouse strains

1996 ◽  
Vol 26 (2) ◽  
pp. 149-160 ◽  
Author(s):  
J. K. Belknap ◽  
S. R. Mitchell ◽  
L. A. O'Toole ◽  
M. L. Helms ◽  
J. C. Crabbe
Keyword(s):  
Quantitative Trait ◽  
Recombinant Inbred ◽  
Inbred Mouse ◽  
Error Rates ◽  
Mouse Strains ◽  
Type I ◽  
Inbred Mouse Strains ◽  
Type Ii ◽  
Type Ii Error ◽  
Recombinant Inbred Mouse

scholarly journals Reliability and Accuracy of Alternative Default Prediction Models: Evidence from Slovakia

2021 ◽  
Vol 9 (4) ◽  
pp. 65
Author(s):  
Daniela Rybárová ◽  
Helena Majdúchová ◽  
Peter Štetka ◽  
Darina Luščíková
Keyword(s):  
Prediction Models ◽  
Slovak Republic ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Z Score ◽  
Local Conditions ◽  
Default Prediction ◽  
Statistical Hypotheses ◽  
Subsequent Comparison

The aim of this paper is to assess the reliability of alternative default prediction models in local conditions, with subsequent comparison with other generally known and globally disseminated default prediction models, such as Altman’s Z-score, Quick Test, Creditworthiness Index, and Taffler’s Model. The comparison was carried out on a sample of 90 companies operating in the Slovak Republic over a period of 3 years (2016, 2017, and 2018) with a narrower focus on three sectors: construction, retail, and tourism, using alternative default prediction models, such as CH-index, G-index, Binkert’s Model, HGN2 Model, M-model, Gulka’s Model, Hurtošová’s Model, Model of Delina and Packová, and Binkert’s Model. To verify the reliability of these models, tests of the significance of statistical hypotheses were used, such as type I and type II error. According to research results, the highest reliability and accuracy was achieved by an alternative local Model of Delina and Packová. The least reliable results within the list of models were reported by the most globally disseminated model, Altman’s Z-score. Significant differences between sectors were identified.

scholarly journals BANKRUPTCY PREDICTION MODEL WITH ZETAc OPTIMAL CUT-OFF SCORE TO CORRECT TYPE I ERRORS

2005 ◽  
Vol 7 (1) ◽  
pp. 41 ◽  
Author(s):  
Mohamad Iwan
Keyword(s):  
Type I Error ◽  
Bankruptcy Prediction ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Prior Probabilities ◽  
Prediction Result ◽  
Optimum Cutting ◽  
Classification Errors ◽  
One Year

This research examines financial ratios that distinguish between bankrupt and non-bankrupt companies and make use of those distinguishing ratios to build a one-year prior to bankruptcy prediction model. This research also calculates how many times the type I error is more costly compared to the type II error. The costs of type I and type II errors (cost of misclassification errors) in conjunction to the calculation of prior probabilities of bankruptcy and non-bankruptcy are used in the calculation of the ZETAc optimal cut-off score. The bankruptcy prediction result using ZETAc optimal cut-off score is compared to the bankruptcy prediction result using a cut-off score which does not consider neither cost of classification errors nor prior probabilities as stated by Hair et al. (1998), and for later purposes will be referred to Hair et al. optimum cutting score. Comparison between the prediction results of both cut-off scores is purported to determine the better cut-off score between the two, so that the prediction result is more conservative and minimizes expected costs, which may occur from classification errors.  This is the first research in Indonesia that incorporates type I and II errors and prior probabilities of bankruptcy and non-bankruptcy in the computation of the cut-off score used in performing bankruptcy prediction. Earlier researches gave the same weight between type I and II errors and prior probabilities of bankruptcy and non-bankruptcy, while this research gives a greater weigh on type I error than that on type II error and prior probability of non-bankruptcy than that on prior probability of bankruptcy.This research has successfully attained the following results: (1) type I error is in fact 59,83 times more costly compared to type II error, (2) 22 ratios distinguish between bankrupt and non-bankrupt groups, (3) 2 financial ratios proved to be effective in predicting bankruptcy, (4) prediction using ZETAc optimal cut-off score predicts more companies filing for bankruptcy within one year compared to prediction using Hair et al. optimum cutting score, (5) Although prediction using Hair et al. optimum cutting score is more accurate, prediction using ZETAc optimal cut-off score proved to be able to minimize cost incurred from classification errors.

scholarly journals Comparing Four Methods for Estimating Tree-Based Treatment Regimes

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Aniek Sies ◽  
Iven Van Mechelen
Keyword(s):  
Simulation Study ◽  
Optimal Treatment ◽  
Treatment Regime ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Treatment Alternative ◽  
Treatment Regimes ◽  
Qualitative Interaction ◽  
Error Probabilities

AbstractWhen multiple treatment alternatives are available for a certain psychological or medical problem, an important challenge is to find an optimal treatment regime, which specifies for each patient the most effective treatment alternative given his or her pattern of pretreatment characteristics. The focus of this paper is on tree-based treatment regimes, which link an optimal treatment alternative to each leaf of a tree; as such they provide an insightful representation of the decision structure underlying the regime. This paper compares the absolute and relative performance of four methods for estimating regimes of that sort (viz., Interaction Trees, Model-based Recursive Partitioning, an approach developed by Zhang et al. and Qualitative Interaction Trees) in an extensive simulation study. The evaluation criteria were, on the one hand, the expected outcome if the entire population would be subjected to the treatment regime resulting from each method under study and the proportion of clients assigned to the truly best treatment alternative, and, on the other hand, the Type I and Type II error probabilities of each method. The method of Zhang et al. was superior regarding the first two outcome measures and the Type II error probabilities, but performed worst in some conditions of the simulation study regarding Type I error probabilities.

Is It Really Robust?

Methodology ◽  
2010 ◽  
Vol 6 (4) ◽  
pp. 147-151 ◽  
Author(s):  
Emanuel Schmider ◽  
Matthias Ziegler ◽  
Erik Danay ◽  
Luzi Beyer ◽  
Markus Bühner
Keyword(s):  
Goodness Of Fit ◽  
Type I Error ◽  
Effect Sizes ◽  
Random Numbers ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Normality Assumption ◽  
Different Types ◽  
Factor Type

Empirical evidence to the robustness of the analysis of variance (ANOVA) concerning violation of the normality assumption is presented by means of Monte Carlo methods. High-quality samples underlying normally, rectangularly, and exponentially distributed basic populations are created by drawing samples which consist of random numbers from respective generators, checking their goodness of fit, and allowing only the best 10% to take part in the investigation. A one-way fixed-effect design with three groups of 25 values each is chosen. Effect-sizes are implemented in the samples and varied over a broad range. Comparing the outcomes of the ANOVA calculations for the different types of distributions, gives reason to regard the ANOVA as robust. Both, the empirical type I error α and the empirical type II error β remain constant under violation. Moreover, regression analysis identifies the factor “type of distribution” as not significant in explanation of the ANOVA results.

When Studies are in Error: Basic Statistical Vocabulary Needed to Understand Clinical Studies

1996 ◽  
Vol 1 (1) ◽  
pp. 25-28 ◽  
Author(s):  
Martin A. Weinstock
Keyword(s):  
Null Hypothesis ◽  
Statistical Power ◽  
Critical Appraisal ◽  
Type I Error ◽  
Statistical Significance ◽  
P Value ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Error Type

Background: Accurate understanding of certain basic statistical terms and principles is key to critical appraisal of published literature. Objective: This review describes type I error, type II error, null hypothesis, p value, statistical significance, a, two-tailed and one-tailed tests, effect size, alternate hypothesis, statistical power, β, publication bias, confidence interval, standard error, and standard deviation, while including examples from reports of dermatologic studies. Conclusion: The application of the results of published studies to individual patients should be informed by an understanding of certain basic statistical concepts.

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