COMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION by Ashish Mishra, Jyoti Mishra, Victor Hugo, Aloísio Vieira Lira Neto

“…KNN 86%, Decision Trees 79%, Logistic Regression 85%, Naive Bayes 86%, and Support Vector Machines 87% can predict heart disease 89% accurately. …”
Get full text

Early detection of gray mold on eggplant leaves using hyperspectral imaging technique by FENG Lei, ZHANG De-rong, CHEN Shuang-shuang, FENG Bin, XIE Chuan-qi, CHEN Youyuan, HE Yong

Subjects: Get full text

Student Performance Prediction Using Machine Learning Algorithms by Esmael Ahmed

“…In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. …”
Get full text

Identification of Rice Varieties Using Machine Learning Algorithms by Murat Koklu, İlkay Çınar

“…For classification, models were created with algorithms using machine learning techniques of k-nearest neighbor, decision tree, logistic regression, multilayer perceptron, random forest and support vector machines. …”
Get full text

Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models by Yunye Shi, Diego Mauricio Yepes Maya, Electo Silva Lora, Albert Ratner

“…This study assesses the effectiveness of various machine learning algorithms in engineering, focusing on a comparative analysis of artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, and regularized regression models. …”
Get full text

Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. …”
Get full text

Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

Get full text

Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients by Tuğba Şentürk, Fatma Latifoğlu, Çiğdem Gülüzar Altıntop, Arzu Yay, Zeynep Burçin Gönen, Gözde Özge Önder, Özge Cengiz Mat, Yusuf Özkul

“…Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). …”
Get full text

Using machine learning algorithms to predict colorectal cancer by Xingjian Xiao, Bo Hong, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Shiyou Liu, Xianglong Xu

Get full text

Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

“…Based on these methods, CHD predictive models were constructed using five different algorithms: K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), Decision Tree, and XGBoost. …”
Get full text

Transforming Cancer Classification: The Role of Advanced Gene Selection by Abrar Yaqoob, Mushtaq Ahmad Mir, G. V. V. Jagannadha Rao, Ghanshyam G. Tejani

Get full text

RELIABILITY ANALYSIS OF REACTION FORCE DEVELOPED IN THE LUBRICATED REVOLUTE JOINT FOR A SLIDER-CRANK SYSTEM INCLUDING JOINT WITH CLEARANCE AND LUBRICATION by ZHAO Kuan, XUE He, CHEN JianJun, QIAO XinZhou

“…The system dynamic model was set up based on Newton-Euler method,The prediction accurary of Support Vector Machine Regression is difficult to reach the target accurary because the selection of parameters isn’t accurate. …”
Get full text

Prediction of rock type from physical and mechanical properties by data mining implementations by Fatih Bayram

“…The paper’s main objective is to present the applicability of data mining algorithms in rock type determination. The physical and mechanical properties of the rocks were evaluated with different data mining algorithms, and the rock types were predicted 95.6% correctly with the model generated with the Support Vector Machine algorithm. …”
Get full text

Error Compensation for Dead Reckoning Based on SVM by Xin LI, Xiaoming WANG, Jianguo WU, Jiwei ZHAO, Jiacheng XIN, Kai CHEN, Bin ZHANG

“…To solve this problem, research was conducted on the application of support vector machine(SVM) for error compensation in dead reckoning. …”
Get full text

Sentiment Classification of Public Perception on LHKPN Using SVM and Naive Bayes by Ahmad Rijal Hermawan Hermawan, Isa Faqihuddin Hanif

“…Sentiment classification was conducted using two machine learning algorithms: Support Vector Machine (SVM) and Naive Bayes. …”
Get full text

Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis by Huan-Zhong Su, Dao-Hui Yang, Long-Cheng Hong, Yu-Hui Wu, Kun Yu, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The AUCs ranged from 0.575 to 0.827 for the nine models. The support vector machine (SVM) algorithm achieved the highest performance with an AUC of 0.827, accompanied by an accuracy of 0.798, precision of 0.792, recall of 0.862, and an F1 score of 0.826. …”
Get full text

Data Mining Approaches in Predicting Entrepreneurial Intentions Based on Internet Marketing Applications by Milan Krivokuća, Mihalj Bakator, Dragan Ćoćkalo, Marijana Vidas-Bubanja, Vesna Makitan, Luka Djordjević, Borivoj Novaković, Stefan Ugrinov

“…Furthermore, a supervised machine learning algorithm, support vector machine (SVM) was used. …”
Get full text

Clinical and neurophysiological predictors of the functional outcome in right-hemisphere stroke by Francesco Di Gregorio, Giada Lullini, Silvia Orlandi, Valeria Petrone, Enrico Ferrucci, Emanuela Casanova, Vincenzo Romei, Fabio La Porta

Get full text

Evaluation of Ecosystem Health Based on AWDO-SVR Algorithm in Shiyang River Basin by WANG Wenchuan, LI Lei, ZHENG Ye, XU Dongmei, XU Lei

“…This paper evaluates the ecological health of Shiyang River Basin by the adaptive wind-driven optimization (AWDO) algorithm and support vector regression (SVR) coupled algorithm for problems in health assessment of watershed ecosystem,finds the optimal parameters of support vector machine (SVM) by AWDO algorithm for uncertainty of parameters from SVM,proposes an evaluation model based on AWDO-SVR algorithm,and evaluates nine indexes such as water resource endowment,water resource development and utilization,and social and economic function of Shiyang River Basin by the model with advantages of fast and simple operation and no need of weight.The results show that the ecological health is sub-health for the upper reaches of Shiyang River,and morbid for the middle and lower reaches respectively.The evaluation result is the same as that of the variable set model,indicating that AWDO-SVR algorithm can be effectively applied to the ecosystem health evaluation of the river basin.…”
Get full text

Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data by Ailing Zhang, Haobo Zhang

“…Feature selection methods, including the least absolute shrinkage and selection operator (LASSO), Boruta, and VSURF, were applied to identify MRI features associated with depression. Support vector machine and random forest algorithms were then used to construct prediction models. …”
Get full text