Binary Split Categorical Feature with Mean Absolute Error Criteria in CART

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life. This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data. The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.

Anti-MDA5+ dermatomyositis (DM) represents a heterogeneous group of patients as diverse individuals display different clinical characteristics, disease progression and complications development. The heterogeneity makes it difficult to classify interstitial lung disease in anti-MDA5 antibodies positive dermatomyositis (anti-MDA5+ DM-ILD). To distinguish and characterize phenotypic subgroups for anti-MDA5+ DM-ILD patients. From August 2014 to March 2022, 188 anti-MDA5+ DM-ILD patients were retrospectively enrolled. 21 HRCT-derived quantitative features were reduced to four principal components through principal component analysis. The missForest algorithm was employed for missing data imputation. Clusters were determined by partitioning around medoids. The classification and regression tree (CART) algorithm was utilized to distinguish between three clusters. The Silhouette Coefficient and Dunn index indicated an optimal cluster number of 3. Patients in cluster 2 (high-risk cluster) have the highest RP-ILD incidence rate (84.9% vs. 18.1% vs. 17.1%, cluster 2 vs. 1 vs. 3, p < 0.001), extremely high early mortality (88.7% vs. 4.3% vs. 2.4%, cluster 2 vs. 1 vs. 3, p < 0.001) and moderate dermato-rheumatologic pattern. Patients in cluster 1 correspond to a pure dermato-rheumatologic pattern (dermato-rheumatologic cluster). Cluster 3 (low-risk cluster) was characterized by not obvious dermato-rheumatologic and the lowest RP-ILD incidence (17.1%) as well as the lowest early mortality (2.4%). The accuracy of the CART algorithm in differentiating clusters was 67.7% in the validation cohort with 56 patients. Principal component 1 was a key feature in the CART algorithm. Sensitivity analyses employing multiple clustering approaches confirmed the robustness of the three-cluster solution by partitioning around medoids. Clustering analysis offers valuable insights into the heterogeneity and clinical implications of anti-MDA5+ DM-ILD. HRCT-derived quantitative features demonstrate significant value for early risk stratification in anti-MDA5+ DM-ILD.

ABSTRAKKabupaten Tangerang mengalami pertumbuhan penduduk pesat yang mendorong perubahan tutupan lahan. Rencana Tata Ruang Wilayah (RTRW) 2020 digunakan sebagai panduan pemanfaatan lahan, dengan klasifikasi utama: lahan terbangun, badan air, vegetasi, dan lahan terbuka. Penelitian ini menganalisis perubahan tutupan lahan menggunakan algoritma Classification and Regression Trees (CART) pada citra Sentinel-2A tahun 2019 dan 2023 di Google Earth Engine. Hasil menunjukkan peningkatan luas bangunan dan lahan terbuka masing-masing 19,866 km² dan 17,877 km², sementara vegetasi dan badan air menurun 33,446 km² dan 4,297 km². Akurasi klasifikasi mendapatkan 89,36% (2019) dan 90,29% (2023). Selain itu, kesesuaian tutupan lahan dengan RTRW meningkat 36,71 km² atau 4%. Hasil ini menunjukkan efektivitas metode CART dalam memantau perubahan tutupan lahan serta relevansinya dengan kebijakan tata ruang di Kabupaten Tangerang.Kata Kunci : Tutupan lahan; Rencana Tata Ruang Wilayah (RTRW), Google Earth Engine (GEE),Classification and Regression Trees (CART), Penginderaan jauh.ABSTRACTTangerang Regency is experiencing rapid population growth that is driving land cover change. The 2020 Regional Spatial Plan (RTRW) is used as a guide for land use, with the main classifications: built-up land, water bodies, vegetation, and open land. This study analyzed land cover change using the Classification and Regression Trees (CART) algorithm on 2019 and 2023 Sentinel-2A images in Google Earth Engine. The results showed an increase in building area and open land of 19.866 km² and 17.877 km² respectively, while vegetation and water bodies decreased by 33.446 km² and 4.297 km². The classification accuracy was 89.36% (2019) and 90.29% (2023). In addition, land cover conformity with the RTRW increased by 36.71 km² or 4%. These results demonstrate the effectiveness of the CART method in monitoring land cover change and its relevance to spatial policy in Tangerang District.Keywords: Land cover, Regional Spatial Plan (RTRW), Supervised Classification, Google Earth Engine (GEE), Classification and Regression Trees (CART), Remote sensing

We aim to provide a tool for independent system operators to detect the collusion and identify the colluding firms by using day-ahead data. In this paper, an approach based on supervised machine learning is presented for collusion detection in electricity markets. The possible scenarios of the collusion among generation firms are firstly identified. Then, for each scenario and possible load demand, market equilibrium is computed. Market equilibrium points under different collusions and their peripheral points are used to train the collusion detection machine using supervised learning approaches such as classification and regression tree (CART) and support vector machine (SVM) algorithms. By applying the proposed approach to a four-firm and ten-generator test system, the accuracy of the proposed approach is evaluated and the efficiency of SVM and CART algorithms in collusion detection are compared with other supervised learning and statistical techniques.

Competition is getting tougher, universities must prepare graduates who can compete in the world of work. The standard of graduates’ profiles that can be used as an assessment is the waiting period. The ideal target of a waiting period is less than or equal to three months. The competence of graduates who can compete in the world of work becomes an assessment of the quality of a university. Several factors that affect the waiting period are the Grade Point Average (GPA), study period, and students’ organization activity. This research was conducted to create a waiting period prediction model using a decision tree based on the factors that affect it. To analyze the waiting period prediction results, the accuracy of the model and the decision tree model is good or not based on the accuracy, sensitivity, and specificity. The decision tree is one of the data mining techniques that can be used for decision-making. In this research, we will use the CART (Classification and Regression Tree) algorithm. In the data mining classification process, data pre-processing will be carried out first, after that the splitting data (training and testing data) will be carried out. Based on the results of the classification tree, the tree size is 10 and has 10 rules. The accuracy of the classification tree's model is 66.67%, 72.97% of sensitivity, and 61.36% of specificity.

This study proposes a new method to simulate spatiotemporal complex multiple land uses by using classification and regression tree algorithm (CART) based CA model. In this model, we use classification and regression tree algorithm to calculate land class conversion probability, and combine neighborhood factor, random factor to extract cellular transformation rules. The overall Kappa coefficient is 0.8014 and the overall accuracy is 0.8821 in the land dynamic simulation results of the three gorges reservoir area from 2000 to 2010, and the simulation results are satisfactory.

Decision trees are a fundamental statistical learning tool for addressing classification and regression problems through a recursive partitioning approach that effectively accommodates numerical and categorical data [1, 2]. The Classification and regression tree (CART) algorithm underlies modern Boosting methodologies such as Gradient boosting machine (GBM), Extreme gradient boosting (XGBoost), and Light gradient boosting machine (LightGBM). However, the standard CART algorithm may require improvement due to its inability to learn from unlabeled data. This study proposes several modifications to incorporate test data into the training phase. Specifically, we introduce a method based on Graph-based semi-supervised learning called “Distance-based Weighting,” which calculates and removes irrelevant records from the training set to accelerate the training process and improve performance. We present Semi-supervised classification and regression tree (Semi-Cart), a new implementation of CART that constructs a decision tree using weighted training data. We evaluated its performance on thirteen datasets from various domains. Our results demonstrate that Semi-Cart outperforms standard CART methods and contributes to statistical learning.

Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5′ and CART algorithms

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

ObjectiveTo assess biomarkers between symptomatic and asymptomatic patients, and to construct a classification and regression tree (CART) algorithm for their discrimination.Patients and Methods136 patients were enrolled. They were symptomatic (high risk) (N = 82, stenosis degree ≥ 50%, proven to be responsible for ischemic stroke the last six months) and asymptomatic (low risk) (N = 54, stenosis degree ≤ 50%). Levels of fibrinogen, matrix metalloproteinase-1 (MMP-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble intercellular adhesion molecule (SiCAM), soluble vascular cell adhesion molecule (SvCAM), adiponectin and insulin were measured on a Luminex 3D platform and their differences were evaluated; subsequently, a CART model was created and evaluated.ResultsAll measured biomarkers, except adiponectin, had significantly higher levels in symptomatic patients. The constructed CART prognostic model had 97.6% discrimination accuracy on symptomatic patients and 79.6% on asymptomatic, while the overall accuracy was 90.4%. Moreover, the population was split into training and test sets for CART validation.ConclusionSignificant differences were found in the biomarkers between symptomatic and asymptomatic patients. The CART model proved to be a simple decision-making algorithm linked with risk probabilities and provided evidence to identify and, therefore, treat patients being at high risk for cardiovascular disease.

ObjectivesAlthough several individual risk factors of frequent outpatient attendance (FOA) have previously been reported, identifying a specific risk profile is needed to provide effective intervention for impoverished citizens with complex...

India is ranked 120th among 122 countries globally in WaterAid&amp;#8217;s water quality index. Regular water quality monitoring is essential to determine which inland water bodies are experiencing depreciating water quality. Long-term trends have been obtained using satellite remote sensing, necessitating multiple image analysis. The computational burden of processing numerous satellite images can be reduced using Google Earth Engine&amp;#8217;s (GEE) cloud computing capabilities. Thorough research was conducted to determine the global spatio-temporal and biochemical factors impacting surface water quality (WQ). The public availability of geospatial datasets and free access to cloud-based geo-computing platforms such as GEE are widely used for spatio-temporal mapping, global surface water monitoring, water quality parametric variation, and real-time forecasting. Many researchers have recently focused on improving data mining and machine learning algorithms to accurately deal with image classification and predictive problems for crop identification and monitoring. In the present study, we propose to define band spectral ratios, spectral band equations, and empirical models for water quality parameters. A few neural network models will be used to (i) query and pre-process satellite earth observations that coincide with the study area, (ii) extract the spectra, and (iii) use spectral band wavelength charts, time-series charts, spatial-distribution maps, and the development of an online dashboard application to visualize the results graphically upon using integrated Landsat (8,9), Sentinel-2A/B and PlanetScope satellite data for the pre-monsoon and post-monsoon seasons in 2023 in the pan-India region. MODIS-TERRA provides LST spatial-temporal monitoring. In this, we have assessed and compared the performance of CART, SVM, and RF ML algorithms. We found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (80.71% overall accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 85.53%, Kappa 0.91). But CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 81.59%, Kappa 0.85). The SAM technique, spectral feature fitting, continuum band removal, and other band-spectral ratio techniques are employed for quantitative hyperspectral data analysis. Specifically, the performance metrics of XGBoost and SGD for both Chl-a (R^2 = 0.818) and Turbidity (R^2 = 0.815) models exhibited robust accuracy. We have also developed a Google API-based JavaScript code that can be tested under complex coastal shores, challenging inundation and variable climatic conditions. Our method provides the end-to-end cloud computing workflow shown in this research, considering cost and computational efficiency for timely information delivery.Keywords: Classification and Regression Trees (CART), Chlorophyll-a (conc), Color dissolved organic matter (CDOM), Composite water management index (CWMI), Environmental Mapping and Analysis Program (EnMAP), Land Surface Temperature (LST), Machine Learning (ML), Random Forest (RF), Spectral Angle Mapper (SAM), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Total Suspended Solids (TSS), Water Quality (WQ), XGBoost (Extreme Gradient Boosting).

This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. This study applies SVM to predict the paying rate index. The objective of this paper is to examine the feasibility of SVM in paying rate forecasting by comparing it with a feed-forward backpropagation (BP) neural network. We choose Gaussian function as its kernel function. The experiment shows that SVM outperforms the feed-forward BP neural network based on the criteria of mean absolute error (MAE), mean absolute percent error (MAPE), mean squared error (MSE) and root mean square error (RMSE). Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast paying rate

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.