Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions by VIJAI M. MOORTHY, BHUPAL N. DHARAMSOTH, VIJAYALAKSHMI MUTHUKARUPPAN, ARUL ELANGO, KALAIARASI GANESAN

“…The true positive rate for the GB algorithm’s predictions of patients was 98.3%. The study hypothesizes that the GB method predicts the Framingham dataset better than other algorithms using 4240 samples.…”
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Development of Digital Twin for FDM Printer With Preventive Cyber-Attack and Control Algorithms by Md Hazrat Ali, Asad Waqar Malik, Nursultan Jyeniskhan, Muhammad Arif Mahmood, Essam Shehab, Frank Liou

“…It also highlights a model predictive control (MPC) algorithm based on a real-time feedback system for controlling the material feed. …”
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Prediction of IPO performance from prospectus using multinomial logistic regression, a machine learning model by Mazin Fahad Alahmadi, Mustafa Tahsin Yilmaz

“…The MLR model had a higher level of accuracy when compared with other machine learning algorithms. By using the model developed here, investors can improve their ability to predict the direction of the return on their investment in an IPO, at least for the first month. …”
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model.…”
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Integrating Genetic Algorithm and Geographically Weighted Approaches into Machine Learning Improves Soil pH Prediction in China by Wantao Zhang, Jingyi Ji, Binbin Li, Xiao Deng, Mingxiang Xu

“…This study integrates Geographic Weighted Regression (GWR) with three ML models (Random Forest, Cubist, and XGBoost) and designs and develops three geographically weighted machine learning models optimized by Genetic Algorithms to improve the prediction of soil pH values. …”
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Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

“…Because predictive algorithms rely on historical data, they capture societal injustices, including racism. …”
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Intelligent classification and prediction of students’ mental health in online learning environments using boosting algorithm and LIWC features by Xiaomin Xu, Tianrong Zhang

“…The model extracts emotional and psychological features from online learning platforms using the LIWC dictionary and integrates multiple weak classifiers using the Boosting algorithm. The performance of the model is enhanced with the Antlion Optimization Algorithm. …”
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Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine by Minjian Wang

“…In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. …”
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Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm by Mohammad Reza Komari Alaei, Reza Rostamzadeh, Kadir Albayrak, Zenonas Turskis, Jonas Šaparauskas

“…Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives. …”
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Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu, Nitesh Dhar Badgayan

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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

“…Advanced machine learning and deep learning algorithms have been effectively used to predict the presence of heart attack based on clinical and laboratory markers. …”
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Prediction of Liquefaction-Induced Lateral Displacements Using Hybrid GBRT and EOA by Arash Ziaie, Bahareh Mehdizadeh, Farzad Safi Jahanshahi, Nazanin Ahmadi, Ali Reza Ghanizadeh

“…This research employs the Gradient Boosting Regression Tree (GBRT) approach, tuned through the Equilibrium Optimization Algorithm (EOA), to estimate LILD. A dataset containing 247 data points was used to build the predictive model. …”
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Long and short term fault prediction using the VToMe-BiGRU algorithm for electric drive systems by Lihui Zheng, Xu Fan, Zongshan Kang, Xinjun Jin, Wenchao Zheng, Xiaofen Fang

“…Specifically, the VToMe algorithm achieves stable detection of medium to long term system faults, while the BiGRU network achieves rapid fault prediction in the short term. …”
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Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network by Yansheng Huang, Atthapol Ngaopitakkul, Suntiti Yoomak

“…., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. …”
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Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression by Deyang Yin, Xiao Zhu, Wanjie Zhang, Jianfeng Zheng

“…To enhance prediction performance, this paper introduces an SOH prediction model based on an improved sparrow algorithm and support vector regression (ISSA-SVR). …”
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An Ultra-Short-Term Wind Power Prediction Method Based on the Fusion of Multiple Technical Indicators and the XGBoost Algorithm by Xuehui Wang, Yongsheng Wang, Yongsheng Qi, Jiajing Gao, Fan Yang, Jiaxuan Lu

“…However, its inherent volatility and unpredictability pose challenges for accurate short-term prediction. This study proposes an ultra-short-term wind power prediction framework that integrates multiple technical indicators with the extreme gradient boosting (XGBoost) algorithm. …”
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Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

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Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm by Xiang Tang, Jianxiao Ma, Shun Zhou, Tianci Shan

“…Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. …”
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