CHAID Algorithm Research Articles

Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models

The logistic regression (LR) and decision tree (DT) models are widely used for prediction analysis in a variety of applications. In the case of landslide susceptibility, prediction analysis is important to predict the areas which have high potential for landslide occurrence in the future. Therefore, the purpose of this study is to analyze and compare landslide susceptibility using LR and DT models by running three algorithms (CHAID, exhaustive CHAID, and QUEST). Landslide inventory maps (762 landslides) were compiled by reference to historical reports and aerial photographs. All landslides were randomly separated into two data sets: 50% were used to establish the models (training data sets) and the rest for validation (validation data sets). 20 factors were considered as conditioning factors related to landslide and divided into five categories (topography, hydrology, soil, geology, and forest). Associations between landslide occurrence and the conditioning factors were analyzed, and landslide-susceptibility maps were drawn using the LR and DT models. The maps were validated using the area under the curve (AUC) method. The DT model running the exhaustive CHAID algorithm (prediction accuracy 90.6%) was better than the DT CHAID (AUC = 90.2%), LR (AUC = 90.1%), and DT QUEST (84.3%) models. The DT model running the exhaustive CHAID algorithm is the best model in this study. Therefore, all models can be used to spatially predict landslide hazards.

基于K-Means和CHAID算法的种质资源数据库与论文关联研究

基于K-Means和CHAID算法的种质资源数据库与论文关联研究

Comparing Predictive Performances of MARS and CHAID Algorithms for Defining Factors Affecting Final Fattening Live Weight in Cultural Beef Cattle Enterprises

Comparing Predictive Performances of MARS and CHAID Algorithms for Defining Factors Affecting Final Fattening Live Weight in Cultural Beef Cattle Enterprises

Algorithm for early detection of a high risk of speech sound disorders in children

Anatomical defects and functional disorders of the masticatory system are often accompanied by speech sound disorders. Speech therapy prophylaxis, as well as prevention of disorders of all parts of the masticatory system, is focused on monitoring of the proper development of anatomy and functions typical of a given developmental period. An early therapeutic intervention is associated with better results. <b>Aim</b>. This study aimed to create a simple tool for dentists to assess the risk of articulation disorders using several preselected physical and functional characteristics. <b>Materials and methods</b>. Three hundred children aged 7-10 years had a dental and speech examination performed. Using the CHAID algorithm upon assessing the disorders of the masticatory system and coexisting speech sound disorders, it was possible to create a decision tree to determine the probability of two disorders occurring at the same time. <b>Results</b>. It was observed that there were many correlations between individual anatomical or functional disorders and presence of speech sound disorders. The strength of those correlations was measured. Mesioclusions, distoclusions, open bites, short lingual frenulum, persistent infantile swallow pattern, mouth breathing, and premature loss of deciduous teeth were all determined to increase the risk of speech sound disorders in children. <b>Conclusions</b>. This algorithm is a simple diagnostic tool for dentists. It enables early detection of a high risk of speech sound disorders. Children with particular malocclusions should undergo speech therapy prophylaxis and be referred to consultations with a speech therapist. Early detection of such abnormalities will allow to avoid not only the intensification and consolidation of speech defects, but also certain pathologies of the oral cavity.<b> (Grudziąż-Sękowska J, Olczak-Kowalczyk D, Zadurska M. Algorithm for early detection of a high risk of speech sound disorders in children. Orthod Forum 2018; 14: 119-29)</b>.

Use of CART and CHAID Algorithms in Karayaka Sheep Breeding

Use of CART and CHAID Algorithms in Karayaka Sheep Breeding

Comparison of commercial and locally developed layers’ performance and egg size prediction using regression tree method

The aim of this study is to compare the hen-day egg production (HDEP) (%), egg size (ES) (g), feed intake (FI) (g), bodyweight (BWT) (kg), body weight gain (BWTG) (g), feed conversion ratio (FCR), feed utilization (FU), and mortality of imported and local commercial egg layers from one to 20 wk of lay, as well as to predict their egg sizes. Two-thousand-six-hundred Hy-line Silver Brown commercial layers (imported) and 2,470 Boschveld indigenous commercial layers (local) were reared from d old to 44 wk of age and fed the same diet throughout. ANOVA was used to test their effects due to the strains, and the CHAID algorithm of regression tree method was used to predict the ES. The local strain came into lay first at wk 20, while the imported came into lay at 21 wk of age. The imported layers performed better in ES, FI, HDEP, BWTG, and FCR (P < 0.05) throughout the laying period, while the local layers were better (P < 0.05) in BWT with higher mortality than the imported. Also, the imported layers performed better in FU (P < 0.05) at wk 4, 8, and 24. Among the imported, the highest ES (58.116 g) was obtained from chickens with HDEP of 13.266 < HDEP < 20.35 and FI of 137.95 < FI< 138.176, while in the local layers, the highest ES (54.802 g) was obtained from chickens with HDEP of 37.876 < HDEP < 47.802.

Supply Chain Enhancement Using Improved Chaid Algorithm for Classifying the Customer Groups

Supply Chain Enhancement Using Improved Chaid Algorithm for Classifying the Customer Groups

Detection of infection preferences of the alfalfa seed chalcid, Bruchophagus roddi Gussakovskiy, 1933 (Hymenoptera: Eurytomidae) in alfalfa (Medicago sativa L.) fields of Igdir, Turkey

This study was conducted to estimate the infection of Bruchophagus roddi in 66 alfalfa fields in Igdir, Turkey during the month of August in 2014-2015, and to determine infection preferences of B. roddi for number of pods in a bunch (NPB) and number of seeds in a pod (NSP). CHAID algorithm was preferred in the prediction of the number of seeds damaged (NSD) through district, year, NPB and NSP. In Aralik district, B. roddi preferred the seeds of varieties with NSP£3.8 (0.858 NSD) at lower rate in 2015. In Karakoyunlu, Tuzluca and Central districts in 2015, varieties with NPB&gt;11 were preferred less but varieties with 9&gt;NPB£11 were mostly preferred. In the Aralýk and Central districts in the 2014, varieties with NPB&gt;10 were preferred less but varieties whose NSP was greater than 7.6 were preferred at the highest proportion by B. roddi in Aralik district in the 2015.

Implementing knowledge discovery in enhancing university student services portfolio management in higher education institutions

Aim: The Student Welfare and Formation Office (SWAFO) of De La Salle University-Dasmarinas, Philippines, used Classification Techniques and Data Envelopment Analysis to analyze student demographics and disciplinary infractions. This paper describes how Knowledge Discovery was used to improve the management of the university’s portfolio of student services.Methodology: To identify the effective Decision-Making Unit (DMU) attributed to colleges, the Data Envelopment Analysis was used. The correlation between student demographics and offending behavior was analyzed using the CHAID algorithm.Findings: The CHAID algorithm is implemented in a software application that is designed to be a predictive analytical software application that predicts student offenses, and a remediation plan is developed as a result. The SWAFO staff and five (5) IT professionals evaluate the software.Implications/Novelty: This research will help SWAFO’s process improvement initiative and delve deeper into the change by utilizing the remediation system plan. This work adds to our understanding of DEA and the design of remediation systems in higher education.

An Application of the CHAID Algorithm to Study the Environmental Impact of Visitors to the Teide National Park in Tenerife, Spain

The significant and complex relationship between visitor numbers to a national park and the environment calls for appropriate policies to be adopted. This paper analyzes the relationship from the perspective of visitors to the Teide National Park (TNP) in Tenerife, aiming to establish strategies to reduce visitors' environmental impacts. This is particularly important as the TNP, with over 3,000,000 visitors in 2015, is the most visited park in Spain and one of the most visited in Europe. An empirical study was conducted during 2016 resulting in 805 valid questionnaires. A CHAID algorithm was then applied to segment visitors according to criterion variables. Findings show the first segmenting variable is transport type, with the car being the most frequently used by visitors. Specifically, the visitor segment coming by car is also associated with the longest stays in the TNP. Regarding the practical and social implications, it is assumed the longer the stay, the greater the environmental impact. These results highlight the need for new transport strategies for the park with improved, less polluting vehicles.

Predictive value of CHAID Algorithm in the diagnosis of malignancy in thyroid nodules with Bethesda III (AUS/FLUS) cytology

Predictive value of CHAID Algorithm in the diagnosis of malignancy in thyroid nodules with Bethesda III (AUS/FLUS) cytology

Predictors of positive opinion about Bach Flower Remedies in adults from three Latin-American countries: An exploratory study

The current study aimed to determine if positive opinions about BFR interacts with spirituality in adults from three Latin-American countries using a CHAID algorithm. Participants were 703 adults from Cuba (n = 319), Costa Rica (n = 252) and Chile (n = 132). Predictors: demographic data, received information, received treatment, spirituality, dispositional optimism and willingness to use a placebo intervention were measured and analyzed. A supervised classification which included a training phase (n = 423) and a test phase (n = 280) was employed. Received information about BFR, spirituality and education were selected as significant predictors of the positive opinion about BFR (>90% of correct classifications).

CHAID Decision Tree: Methodological Frame and Application

Abstract Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into useful knowledge. Data analysis adapted to these changes when data mining was developed as an approach to data analysis from different perspectives which reveals significant hidden regularities. This paper presents conceptual characteristics of decision tree, an important data mining method which is, due to its explorative nature, exceptionally suitable for detection of data structure when analysing various problem situations. The empirical section of the paper demonstrates applicative characteristics of this method using CHAID algorithm in leadership studies: an interdependence of selected personal characteristics and the manager’s leadership style has been investigated. The aim of the paper is to develop a classification model for identification of the dominant leadership style. The study was conducted on the sample of 417 managers of privately owned small-sized enterprises in Serbia, using a specially designed questionnaire. The classification model identified the set of six statistically significant personal characteristics as predictors of dominant leadership style.

Performance analysis of classification algorithms on early detection of liver disease

The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area.In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB=[10.900–1.200], ALB [4.00–4.300], SGPT=[34–37], TB=[0.600–1.200] (by boosted C5.0), A/G=[1.180–1.390], as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature.

Emotional and Altruistic Values as Drivers for a Loyalty-Based Segmentation in Retailing: An Approach to Postrecession Spanish Apparel Consumers

ABSTRACTResearch on consumer behavior has supported the usefulness of the experiential approach to explain shopping and consumption in the context of retailing. The present article aims at assessing the soundness of an extended experiential value concept for segmenting apparel retail customers in terms of their loyalty toward the store. A CHAID algorithm based on a sample of postrecession Spanish apparel retail customers has identified three segments of customers with differentiated levels of loyalty toward the store: play (emotional value) and ethics (altruistic value) are the most relevant variables for defining these segments.

Prediction of egg weight from egg quality characteristics via ridge regression and regression tree methods

This study was conducted on 2049 eggs, collected from commercial white layer hybrids, with the purpose of predicting egg weight (EW) from egg quality characteristics such as shell weight (SW), albumen weight (AW), and yolk weight (YW). In the prediction of EW, ridge regression (RR), multiple linear regression (MLR), and regression tree analysis (RTM) methods were used. Predictive performance of RR and MLR methods was evaluated using the determination coefficient (R2) and variance inflation factor (VIF). R2 (%) coefficients for RR and MLR methods were found as 93.15% and 93.4% without multicollinearity problems due to very low VIF values, varying from 1 to 2, respectively. Being a visual, non-parametric analysis technique, regression tree method (RTM) based on CHAID algorithm performed a very high predictive accuracy of 99.988% in the prediction of EW. The highest EW (71.963 g) was obtained from eggs with AW > 41 g and YW > 17 g. The usability of RTM due to a very great accuracy of 99.988 (%R2) in the prediction of EW could be advised in practice in comparison with the ridge regression and multiple linear regression analysis techniques, and might be a very valuable tool with respect to quality classification of eggs produced in the poultry science.

Classification tree for the assessment of sedentary lifestyle among hypertensive.

To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

Knowledge discovery from patients' behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services.

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers.

Application of Intelligent Data Mining Approach in Securing the Cloud Computing

Cloud computing is a modern term refers to a model for emerging computing, where it is possible to use machines in large data centers for delivering services in a scalable manner, so corporations has become in need for large scale inexpensive computing. Recently, several governments have begun to utilize cloud computing architectures, applications and platforms for meeting the needs of their constituents and delivering services. Security occupies the first rank of obstacles that face cloud computing for governmental agencies and businesses. Cloud computing is surrounded by many risks that may have major effects on services and information supported via this technology. Also, Cloud Computing is one of the promising technology in which the scientific community has recently encountered. Cloud computing is related to other research areas such as distributed and grid computing, Service-Oriented Architecture, and virtualization, as cloud computing inherited their limitations and advancements. It is possible to exploit new opportunities for security. This paper aim is to discuss and analyze how achieve mitigation for cloud computing security risks as a basic step towards obtaining secure and safe environment for cloud computing. The results showed that, Using a simple decision tree model Chaid algorithm security rating for classifying approach is a robust technique that enables the decision-maker to measure the extent of cloud securing, and the provided services. It was proved throughout this paper that policies, standards, and controls are critical in management process to safeguard and protect the systems as well as data. The management process should analyze and understand cloud computing risks for protecting systems and data from security exploits

Application of classification trees in determining the impact of phenotypic factors on conception to first service in Holstein cattle

The aim of the study was to evaluate the impact of phenotypic factors on the probability of conception to first service. The study analysed 49 317 lactation records gathered between 1995 and 2007 in Iran, detailing six consecutive lactations in 13 herds of Holstein cows. The impact of phenotypic factors on the conception to first service was evaluated using the classification tree technique. The CHAID algorithm with the Pearson Chi-squared test was used as the division criterion. The resulting tree was six levels deep and consisted of 22 leaves. The greatest divisions in the tree occurred based on the year of calving (five divisions), herd and parity (four divisions each), level of milk production and age at calving (three divisions each). Fewer divisions occurred based on the month when the first insemination was performed (two divisions). The tree structure obtained in the present study allowed identification of combinations of levels of factors associated with highest probabilities of conception. The highest rate of conception to first service was recorded for cows that: while calving were younger than 5 years old; were in herds 2, 4, 5, 7, 8, 12 and 14; and were inseminated in January, February, March, April, May and November. Those cows were at least in their third lactation.