Chi-square automatic interaction detector decision tree analysis model: Predicting cefmetazole response in intra-abdominal infection

Hiranuma, Masumi; Kobayashi, Daiki; Yokota, Kyoko; Yamamoto, Kazuki

doi:10.1016/j.jiac.2022.09.002

Cited by 6 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CHAID model is a data mining technique that highly facilitates graphical presentation that provides an easy way for data interpretation in the medical field, with notable advantages over other models such as logistic regression analysis. In addition, it provides the ability to deal with multiple nodes, since it emerges all variables in a given data set [20].…”

Section: Principal Findingsmentioning

confidence: 99%

Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study

Hani

Ahmad

2023

JMIR Cardio

View full text Add to dashboard Cite

Background Many current studies have claimed that the actual risk of heart disease among women is equal to that in men. Using a large machine learning algorithm (MLA) data set to predict mortality in women, data mining techniques have been used to identify significant aspects of variables that help in identifying the primary causes of mortality within this target category of the population. Objective This study aims to predict mortality caused by heart disease among women, using an artificial intelligence technique–based MLA. Methods A retrospective design was used to retrieve big data from the electronic health records of 2028 women with heart disease. Data were collected for Jordanian women who were admitted to public health hospitals from 2015 to the end of 2021. We checked the extracted data for noise, consistency issues, and missing values. After categorizing, organizing, and cleaning the extracted data, the redundant data were eliminated. Results Out of 9 artificial intelligence models, the Chi-squared Automatic Interaction Detection model had the highest accuracy (93.25%) and area under the curve (0.825) among the build models. The participants were 62.6 (SD 15.4) years old on average. Angina pectoris was the most frequent diagnosis in the women's extracted files (n=1,264,000, 62.3%), followed by congestive heart failure (n=764,000, 37.7%). Age, systolic blood pressure readings with a cutoff value of >187 mm Hg, medical diagnosis (women diagnosed with congestive heart failure were at a higher risk of death [n=31, 16.58%]), pulse pressure with a cutoff value of 98 mm Hg, and oxygen saturation (measured using pulse oximetry) with a cutoff value of 93% were the main predictors for death among women. Conclusions To predict the outcomes in this study, we used big data that were extracted from the clinical variables from the electronic health records. The Chi-squared Automatic Interaction Detection model—an MLA—confirmed the precise identification of the key predictors of cardiovascular mortality among women and can be used as a practical tool for clinical prediction.

show abstract

Section: Principal Findingsmentioning

confidence: 99%

Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study

Hani

Ahmad

2023

JMIR Cardio

View full text Add to dashboard Cite

show abstract

“…The chi-square test in statistics is used to evaluate if two occurrences are independent. From the data of two variables, we may get the observed count O and the anticipated count E. The difference between the expected count E and the actual count O is calculated using the Chi-Square formula [28].…”

Section: B Algorithms Descriptionmentioning

confidence: 99%

Evaluation of Bearing Fault Detection on Different _K-Folds using Deep Learning Ensemble Models

Deep Prakash Singh

2023

IJRITCC

View full text Add to dashboard Cite

One of the most crucial parts of contemporary machinery and industrial equipment is the induction motor. Therefore, it is essential to create a fault diagnosis system that can identify induction motor problems and operating circumstances before they become serious. In this study, an induction motor's defect diagnosis is carried out in three different states, including normal, rotor fault, and bearing fault. The suggested fault diagnostic system is also described, along with a GUI. The experimental findings support the suitability of the suggested approach for rotor and bearing defects in induction motor diagnosis. A GUI for defect diagnostics was also created and used in a real-world setting. We have used Chi-Square method for high score attributes values. For the normal, rotor fault, and bearing fault states of induction motors identified by DBN, CNN, SNN, SVM and RF respectively, the fault detection system's accuracy in the actual world. In the experiment, we find Algorithms model-II, K-Folds (5, 10 & 15) , Accuracy (%), Training loss, Validation loss value for RF-SVM-CNN are 89.2, 0.260013, 0.304936 for k fold 5, 98.4, 0.155960, 0.154133 for k-fold 10 and 98.3, 0.155759, 0.144127 for k- fold 15 respectively.

show abstract

“…One of the possible uses is in healthcare to predict the admission in the Intensive Care Unit (ICU) of hospital of patients with COVID-19 at the point of admission to the hospital, trying to have a very good use of resources in those places [43]. Ghane et al [44] use them to diagnose Parkinson's disease, Elhazmi et al [45] for predicting mortality in critically ill adults with COVID-19 in the ICU, Hiranuma et al [46] for assessing the effectiveness of treatment in intra-abdominal infections using cefmetazole, and in disease diagnosis, such as genomics or cancer, as explained in [47]. Other uses are in security [48] or in high efficiency video coding [49].…”

Section: Data Mining Applicationsmentioning

confidence: 99%

What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey

García

Álvarez-Fernández²

2022

BDCC

View full text Add to dashboard Cite

Big Data has changed how enterprises and people manage knowledge and make decisions. However, when talking about Big Data, so many times there are different definitions about what it is and what it is used for, as there are many interpretations and disagreements. For these reasons, we have reviewed the literature to compile and provide a possible solution to the existing discrepancies between the terms Data Analysis, Data Mining, Knowledge Discovery in Databases, and Big Data. In addition, we have gathered the patterns used in Data Mining, the different phases of Knowledge Discovery in Databases, and some definitions of Big Data according to some important companies and organisations. Moreover, Big Data has challenges that sometimes are the same as its own characteristics. These characteristics are known as the Vs. Nonetheless, depending on the author, these Vs can be more or less, from 3 to 5, or even 7. Furthermore, the 4Vs or 5Vs are not the same every time. Therefore, in this survey, we reviewed the literature to explain how many Vs have been detected and explained according to different existing problems. In addition, we detected 7Vs, three of which had subtypes.

show abstract

Chi-square automatic interaction detector decision tree analysis model: Predicting cefmetazole response in intra-abdominal infection

Cited by 6 publications

References 40 publications

Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study

Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study

Evaluation of Bearing Fault Detection on Different _K-Folds using Deep Learning Ensemble Models

What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey

Contact Info

Product

Resources

About