Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. …”
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Development and validation of machine learning models predicting hospitalizations of hypertensive patients over 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitsky, O. M. Drapkina, A. V. Gusev

“…To develop models for predicting hospitalizations of hypertensive (HTN) over 12 months using machine learning algorithms and to validate them using real-world practice data.Material and methods. …”
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Lifetime Prediction of Power IGBT Module by WANG Yan-gang, Chamund Dinesh, LI Shi-ping, Jones Steve, DOU Ze-chun, XIN Lan-yuan, LIU Guo-you

“…The Weibull methodology for power cycling test data and some reported typical lifetime models were firstly discussed. Then, lifetime prediction procedures were presented including the conversion of mission profile to temperature profile, the temperature cycles counting by Rainflow algorithm, and lifetime calculating based on the fatigue linear accumulation damage theory and lifetime models. …”
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Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

“…Additionally, a genetic algorithm (GA) was utilized for feature selection and SHAP (SHapley Additive exPlanations) for interpreting the contributions of each employed feature. …”
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Genetic prediction of male pattern baldness. by Saskia P Hagenaars, W David Hill, Sarah E Harris, Stuart J Ritchie, Gail Davies, David C Liewald, Catharine R Gale, David J Porteous, Ian J Deary, Riccardo E Marioni

“…By splitting the cohort into a discovery sample of 40,000 and target sample of 12,000, we developed a prediction algorithm based entirely on common genetic variants that discriminated (AUC = 0.78, sensitivity = 0.74, specificity = 0.69, PPV = 59%, NPV = 82%) those with no hair loss from those with severe hair loss. …”
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AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector by Şenda Yıldırım, Ahmet Deniz Yücekaya, Mustafa Hekimoğlu, Meltem Ucal, Mehmet Nafiz Aydin, İrem Kalafat

“…By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. …”
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Conformational ensembles for protein structure prediction by Jiaan Yang, Wen Xiang Cheng, Peng Zhang, Gang Wu, Si Tong Sheng, Junjie Yang, Suwen Zhao, Qiyue Hu, Wenxin Ji, Qiong Shi

“…The P53_HUMAN as a well-known protein and LEF1_HUMAN and Q8GT36_SPIOL as typical disordered proteins are token as the benchmark to evaluate the predicted outcomes. The results demonstrated an effective algorithm and biological meaningful process well to predict protein multiple conformation structures.…”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. This experimental research focuses on evaluating existing robust cross-validation variants to address the issues of validating credit default models. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The machine learning algorithms can handle high-dimensional nonlinear data with complex interactions, outperform traditional statistical models in ecology, and have been effectively used for plant classification, phenology detection, crop growth detection, and yield prediction. …”
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Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…Particle Swarm Optimization, Grey Wolf Optimizer, Pendulum Search Algorithm, and Whale Optimization Algorithm were employed to reduce the feature space, removing approximately 45% of clinical and analytical variables while maintaining high recall for the minority class of patients experiencing hypotension. …”
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Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic by Eleftheria Koutsaki, George Vardakis, Nikos Papadakis

“…We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. …”
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Novel three‐dimensional ECG algorithm for reliable screening for cardiac amyloidosis by Amir A. Mahabadi, Jan Knobeloch, Viktoria Backmann, Lars Michel, Markus S. Anker, Reza Wakili, Christian Fach, Stefan D. Anker, Tienush Rassaf

“…In the derivation cohort, the AI algorithm reached a sensitivity of 85%, a specificity of 89%, a positive predictive value of 91%, and a negative predictive value of 87%. …”
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Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction. by Ruiyu Zhan

“…Our novel approach, LGWO-BP, is tailored to augment the precision of cancer prognosis predictions. We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
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Score-Predictive Scale for Assessing the Risk of Inguinal and Femoral Hernias Incarceration by B. V. Sigua, V. P. Zemlyanoy, A. A. Kozobin, D. S. Semin

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A lightweight detection algorithm of PCB surface defects based on YOLO. by Shiwei Yu, Feng Pan, Xiaoqiang Zhang, Linhua Zhou, Liang Zhang, Jikui Wang

“…The results indicated that when comparing our model with the original model, there was a 47.2% reduction in the model's parameter count, a 48.5% reduction in GFLOPs, a 42.4% reduction in Weight, a 2.0% reduction in FPS, and a 2.4% rise in mAP. …”
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Active noise control of refrigerator based on cascaded notch feedback algorithm by Chaoping Gui

“…The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.…”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…<b>Methods:</b> We analyzed the GSE77938 (PRJNA312169) dataset for differential gene expression (DGE) and performed gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify enriched pathways in keratoconus (KTCN) versus controls. Machine learning algorithms were then used to analyze the gene sets, with SHapley Additive exPlanations (SHAP) applied to assess the contribution of key feature genes in the model’s predictions. …”
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Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles by Bo Wang, Junchao Peng, Jing Zhou, Liming Zhao

“…A novel adaptive event-triggered nonlinear model predictive control (AET-NMPC) algorithm is proposed and compared with traditional Cascaded Proportional–Integral–Derivative (PID) control and event-triggered cascaded PID control algorithms. …”
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Interpreting Predictive Models through Causality: A Query-Driven Methodology by Mahdi Hadj Ali, Yann Le Biannic, Pierre-Henri Wuillemin

“…Machine learning algorithms have been widely adopted in recent years due to their efficiency and versatility across many fields. …”
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A predictive model for damp risk in english housing with explainable AI by Gulala Aziz, Adam Hardy

“…This study develops a predictive model for damp risk, using 2,073 inspection records from a housing association across 125 local authorities. …”
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