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Effective Automated Prediction of Vertebral Column Pathologies Based on logistic model tree with SMOTE Preprocessing

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JOURNAL OF MEDICAL SYSTEMS 2014年第5期38卷 50-50页

作者： Karabulut, Esra Mahsereci Ibrikci, Turgay Gaziantep Univ Vocat High Sch Higher Educ TR-27310 Gaziantep Turkey Cukurova Univ Dept Elect Elect Engn TR-01330 Adana Turkey

This study develops a logistic model tree based automation system based on for accurate recognition of types of vertebral column pathologies. Six biomechanical measures are used for this purpose: pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. A two-phase classification model is employed in which the first step is preprocessing the data by use of Synthetic Minority Over-sampling Technique (SMOTE), and the second one is feeding the classifier logistic model tree (LMT) with the preprocessed data. We have achieved an accuracy of 89.73 %, and 0.964 Area Under Curve (AUC) in computer based automatic detection of the pathology. This was validated via a 10-fold-cross-validation experiment conducted on clinical records of 310 patients. The study also presents a comparative analysis of the vertebral column data with the use of several machine learning algorithms.

关键词： Vertebral column pathologies logistic model tree SMOTE Biomechanical measures

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Industrial Part Faults Prediction for Nonlinearity and Implied Temporal Sequences

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PROCESSES 2025年第2期13卷 436-436页

作者： Zhang, Shuyu Zhang, Mengyi Bo, Cuimei Wang, Cunsong Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211800 Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 210009 Peoples R China

The ability to preemptively identify potential failures in industrial parts is crucial for minimizing downtime, reducing maintenance costs and ensuring system reliability and safety. However, challenges such as data nonlinearity, temporal dependencies, and imbalanced datasets complicate accurate fault prediction. In this study, we propose a novel combined approach that integrates the logistic model tree Forest (LMT) with Stacked Long Short-Term Memory (LSTM) networks, addressing these challenges effectively. This hybrid method leverages the decision-making capability of the LMT and the temporal sequence learning ability of Stacked LSTM to improve fault prediction accuracy. Additionally, to tackle the issues posed by imbalanced datasets and noise, we employ the ENN-SMOTE (Edited Nearest Neighbors-Synthetic Minority Over-sampling Technique), a technique for data preprocessing, which enhances data balance and quality. Experimental results show that our approach significantly outperforms traditional methods, achieving a fault prediction accuracy of up to 98.2%. This improvement not only demonstrates the effectiveness of the combined model but also highlights its potential for real-world industrial applications, where high accuracy and reliability are paramount.

关键词： fault prediction logistic model tree clarification LSTM

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Evaluation of an Image-based Classification model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset

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CLINICAL NEURORADIOLOGY 2025年第1期35卷 151-158页

作者： Amador, K. Kniep, H. Fiehler, J. Forkert, N. D. Lindner, T. Univ Calgary Dept Radiol & Clin Neurosci Calgary AB Canada Univ Hosp Hamburg Eppendorf Dept Diagnost & Intervent Neuroradiol Martinistr 52 D-20251 Hamburg Germany

PurposeGlioma is a complex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and *** total of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a 3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using a machine learning framework consisting of ReliefF feature ranking and logistic model tree classification *** results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 +/- 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 +/- 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 +/- 32.72 selected *** and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

关键词： Glioma Radiomics Arterial Spin Labeling ReliefF logistic model tree

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An improved potential landslide hazard points evaluating method considering the heterogeneity of environmental features

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第7期20卷 7951-7968页

作者： Zhu, S. Kong, R. Luo, X. Xu, Z. Zhu, F. China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Peoples R China Natl Engn Res Ctr Geog Informat Syst Wuhan 430074 Peoples R China Wuhan Zhongdi Yunshen Technol Co Ltd Wuhan 430000 Peoples R China

Landslide causes great damage to the environment and threatens the safety of people's life and property. The environmental features show heterogeneous changes in an area, but it is rarely used in the evaluation of landslide susceptibility. In this study, cluster analysis and ensemble learning were combined to verify the importance of environmental heterogeneity, and an improved landslide susceptibility mapping method (FCM-RF) was proposed considering the heterogeneity of environmental features. Taking Qichun County, China, as a study area, this paper analyzed the correlation and importance of thirteen landslide causal factors. The landslide and non-landslide samples were used for cluster analysis using k-means and fuzzy C-means, respectively. The study area was divided into several areas, and the type represented by each area was used as one of the input factors of the landslide model. Three methods, random forest, logistic model tree and Bagging, were selected to establish landslide susceptibility models. Finally, receiver operation curve and five statistic indicators (accuracy, precision, recall, specificity and F-measure) were employed to compare and validate the performance of these methods. The results showed that the accuracy of the three models was improved by about 2% after considering the heterogeneity factor and the FCM-RF (AUC = 0.8872) model with better performance was used for landslide susceptibility mapping. Therefore, considering the heterogeneity of environmental features can effectively improve the performance of landslide model, which will be helpful for landslide prediction.

关键词： Bagging Environmental heterogeneity Landslide susceptibility mapping logistic model tree Random forest

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Improved tree-based machine learning algorithms combining with bagging strategy for landslide susceptibility modeling

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ARABIAN JOURNAL OF GEOSCIENCES 2022年第2期15卷 1-19页

作者： Zhang, Tingyu Quevedo, Renata Pacheco Wang, Huanyuan Fu, Quan Luo, Dan Wang, Tao de Oliveira, Guilherme Garcia Guasselli, Laurindo Antonio Renno, Camilo Daleles Minist Nat Resources Key Lab Degraded & Unused Land Consolidat Engn Xian 710064 Shaanxi Peoples R China Shaanxi Prov Land Engn Construct Grp Co Ltd Inst Land Engn & Technol Xian 710064 Shaanxi Peoples R China Natl Inst Space Res INPE Earth Observat & Geoinformat Div BR-12227010 Sao Jose Dos Campos SP Brazil Design Inst Co Ltd Shaanxi Prov Land Engn Construct Grp Land Survey Xian 710075 Shaanxi Peoples R China Univ Fed Rio Grande do Sul State Ctr Res Remote Sensing & Meteorol BR-90501970 Porto Alegre RS Brazil

Landslide is considered one of the most dangerous natural hazards. Reasonable landslide susceptibility mapping can aid decision makers in landslide prevention. For this reason, based on the field survey data of landslide in Chenggu County, Shaanxi Province, China, 15 conditioning factors (altitude, slope, aspect, plan curvature, profile curvature, SPI, TWI, distance to roads, distance to rivers, distance to faults, rainfall, NDVI, soil, lithology, and land use) were selected and quantified by the certainty factor index. Then, 184 landslides data were divided into training and validation datasets according to the ratio of 7/3. Based on the GIS platform, three hybrid tree-based models, namely decision tree (DT), logistic model tree (LMT), and reduced error pruning tree (REPT), were established. Additionally, the bagging method was applied to build three bag-hybrid tree-based models: Bag-DT, Bag-LMT, and Bag-REPT. Finally, the landslide susceptibility maps were produced, and statistical indexes, seed cell area index and the ROC curve, were used for model validation and comparison. The results showed that the bagging method can significantly improve the classification ability of hybrid models. Furthermore, the Bag-REPT presented the best performance, with an accuracy value of 92.5%, being a suitable model for landslide susceptibility mapping in the study area.

关键词： Landslide susceptibility Decision tree logistic model tree Reduced error pruning tree Hybrid models Bagging strategy

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Significant factors associated with malaria spread in Thailand: a cross-sectional study

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JOURNAL OF HEALTH RESEARCH 2022年第3期36卷 515-523页

作者： Krainara, Patcharaporn Dumrongrojwatthana, Pongchai Bhattarakosol, Pattarasinee Chulalongkorn Univ Fac Sci Bangkok Thailand Chulalongkorn Univ Fac Sci Math & Comp Sci Bangkok Thailand

Purpose This paper aims to uncover new factors that influence the spread of malaria. Design/methodology/approach The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution. Findings This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree. Originality/value This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.

关键词： Malaria distribution Malaria control logistic model tree Risk factors Risk model Thailand

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Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

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NATURAL HAZARDS 2021年第3期108卷 3229-3251页

作者： Chinh Luu Quynh Duy Bui Costache, Romulus Luan Thanh Nguyen Thu Thuy Nguyen Tran Van Phong Hiep Van Le Binh Thai Pham Natl Univ Civil Engn Fac Hydraul Engn Hanoi Vietnam Natl Univ Civil Engn Dept Geodesy Hanoi Vietnam Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Vietnam Acad Water Resources Key Lab River & Coastal Engn Hanoi Vietnam Univ Technol Sydney Ctr Technol Water & Wastewater Sch Civil & Environm Engn Sydney NSW 2007 Australia Vietnam Acad Sci & Technol Inst Geol Sci Hanoi Vietnam Univ Transport Technol Hanoi Vietnam

Vietnam's central coastal region is the most vulnerable and always at flood risk, severely affecting people's livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD tree), logistic model tree (LM tree), reduced-error pruning tree (REP tree), J48 decision tree (J48) and Naive Bayes tree (NB tree);historical flood marks;and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016;and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models' validation suggests that all models have good performance: AD tree (AUC = 0.968), LM tree (AUC = 0.967), REP tree (AUC = 0.897), J48 (AUC = 0.953), and NB tree (AUC = 0.986). Out of these, NB tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km(2) (78.8 % area) with a very low flooding hazard, 391 km(2) (4.9 % area) with a low flooding hazard, 224 km(2) (2.8 % area) with a moderate flooding hazard, 243 km(2) (3.1 %) with a high flooding hazard, and 829 km(2) (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.

关键词： Flood susceptibility map Alternating decision tree logistic model tree Reduced-error pruning tree J48 Naive Bayes tree

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Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm-A Machine learning approach 28

Friction stir welding tool condition monitoring using vibrat...

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28th International Conference on Processing and Fabrication of Advanced Materials (PFAM)

作者： Balachandar, K. Jegadeeshwaran, R. Vellore Inst Technol Sch Mech Engn Chennai 600127 Tamil Nadu India Prathyusha Engn Coll Dept Mech Engn Thiruvallur 602025 Tamil Nadu India

Friction stir welding (FSW) is a new kind of solid-state welding technique. Rigid and reliable joints in intricate shapes are possible with FSW. This type of welding process is frequently used in many commercial applications like automobile, shipbuilding, aerospace, and many more. The monitoring of the welding tool condition is essential for the inline process to identify and avoid the early defect of the workpiece and the breakdown due to the increase of the ideal condition of the machinery. Condition monitoring of the FSW tool is an advanced and novel predictive maintenance technique, in which the real-time vibration data are collected from the FSW machine under different operating conditions using an accelerometer sensor. The acquired vibration signals were analyzed using the machine learning approaches through feature extraction and feature classification. This paper aims the vibration analysis based on FSW tool condition monitoring using machine learning algorithms such as decision tree, logistic model tree (LMT), Hoeffeding, and Random forest. Among all classifiers, Random Forest gives better results. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 28th International Conference on Processing and Fabrication of Advanced Materials.

关键词： Friction stir welding logistic model tree Statistical features Random forest Confusion matrix

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Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units

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SENSORS 2021年第14期21卷 4625-4625页

作者： Reilly, Brian Morgan, Oliver Czanner, Gabriela Robinson, Mark A. Liverpool John Moores Univ Sch Comp Sci & Math Liverpool L3 3AF Merseyside England Liverpool John Moores Univ Sch Sport & Exercise Sci Liverpool L3 3AF Merseyside England Celt Football Club Celtic Pk Glasgow G40 3RE Lanark Scotland

Changes of direction (COD) are an important aspect of soccer match play. Understanding the physiological and biomechanical demands on players in games allows sports scientists to effectively train and rehabilitate soccer players. COD are conventionally recorded using manually annotated time-motion video analysis which is highly time consuming, so more time-efficient approaches are required. The aim was to develop an automated classification model based on multi-sensor player tracking device data to detect COD > 45 degrees. Video analysis data and individual multi-sensor player tracking data (GPS, accelerometer, gyroscopic) for 23 academy-level soccer players were used. A novel 'GPS-COD Angle' variable was developed and used in model training;along with 24 GPS-derived, gyroscope and accelerometer variables. Video annotation was the ground truth indicator of occurrence of COD > 45 degrees. The random forest classifier using the full set of features demonstrated the highest accuracy (AUROC = 0.957, 95% CI = 0.956-0.958, Sensitivity = 0.941, Specificity = 0.772. To balance sensitivity and specificity, model parameters were optimised resulting in a value of 0.889 for both metrics. Similarly high levels of accuracy were observed for random forest models trained using a reduced set of features, accelerometer-derived variables only, and gyroscope-derived variables only. These results point to the potential effectiveness of the novel methodology implemented in automatically identifying COD in soccer players.

关键词： change of direction movements Global Positioning System accelerometer gyroscope machine learning random forest logistic model tree

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Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 190卷 106476-106476页

作者： Marin, Diego Bedin Ferraz, Gabriel Araujo e Silva Santana, Lucas Santos Barbosa, Brenon Diennevan Souza Barata, Rafael Alexandre Pena Osco, Lucas Prado Ramos, Ana Paula Marques Guimaraes, Paulo Henrique Sales Univ Fed Lavras Dept Agr Engn BR-37200900 Lavras MG Brazil Univ Western Sao Paulo Fac Engn & Architecture & Urbanism Rodovia Raposo TavaresKm 572 BR-19067175 Presidente Prudente SP Brazil Univ Western Sao Paulo Postgrad Program Environm & Reg Dev Rodovia Raposo TavaresKm 572 BR-19067175 Presidente Prudente SP Brazil Univ Fed Lavras Dept Stat BR-37200000 Lavras MG Brazil

Coffee leaf rust (CLR) is one of the most devastating leaf diseases in coffee plantations. By knowing the symptoms, severity, and spatial distribution of CLR, farmers can improve disease management procedures and reduce losses associated with it. Recently, Unmanned Aerial Vehicles (UAVs)-based images, in conjunction with machine learning (ML) techniques, helped solve multiple agriculture-related problems. In this sense, vegetation indices processed with ML algorithms are a promising strategy. It is still a challenge to map severity levels of CLR using remote sensing data and an ML approach. Here we propose a framework to detect CLR severity with only vegetation indices extracted from UAV imagery. For that, we based our approach on decision tree models, as they demonstrated important results in related works. We evaluated a coffee field with different infestation classes of CLR: class 1 (from 2% to 5% rust);class 2 (from 5% to 10% rust);class 3 (from 10% to 20% rust), and;class 4 (from 20% to 40% rust). We acquired data with a Sequoia camera, producing images with a spatial resolution of 10.6 cm, in four spectral bands: green (530-570 nm), red (640-680 nm), red-edge (730-740 nm), and nearinfrared (770-810 nm). A total of 63 vegetation indices was extracted from the images, and the following learners were evaluated in a cross-validation method with 10 folders: logistic model tree (LMT);J48;Extratree;REPtree;Functional trees (FT);Random tree (RT), and;Random Forest (RF). The results indicated that the LMT method contributed the most to the accurate prediction of early and several infestation classes. For these classes, LMT returned F-measure values of 0.915 and 0.875, thus being a good indicator of early CLR (2 to 5% of rust) and later stages of CLR (20 to 40% of rust). We demonstrated a valid approach to model rust in coffee plants using only vegetation indices and ML algorithms, specifically for the disease's early and later stages. We concluded that the proposed fra

关键词： Multispectral imagery Precision agriculture Plant disease logistic model tree

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