SPAM-XAI compared with previous models. by Mohd Mustaqeem (19106494)

“…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …”

Supplementary file 1_Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.docx by Lian Li (49049)

“…Objective<p>This study utilizes real-world data from primary membranous nephropathy (PMN) patients to preliminarily develop a venous thromboembolism (VTE) risk prediction model with machine learning. …”

Supplementary file 1_Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.docx by Xiaohui Guo (131711)

Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

“…An Affymetrix HTA2.0 array and qRT-PCR were applied to screen new specific lncRNA markers for TB in individual nucleated cells from host peripheral blood. A ML algorithm was established to combine the patients’ EHR information and lncRNA data via logistic regression models and nomogram visualization to differentiate PTB from suspected patients of the selection cohort.…”

Data_Sheet_1_Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis.docx by Anling Xiao (11252973)

Data_Sheet_1_Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.PDF by Nguyen Thanh Nhu (16400859)

Proposed method approach. by Muhammad Usman Tariq (11022141)

“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”

Descriptive statistics. by Muhammad Usman Tariq (11022141)

“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”

Inconsistency concept for a triad (2, 5, 3). by Waldemar W. Koczkodaj (22008783)

“…The proposed regeneration method emulates three primary phases of a biological process: identifying the most damaged areas (by identifying inconsistencies in the pairwise comparison matrix), cell proliferation (filling in missing data), and stabilization (optimization of global consistency). …”

Data_Sheet_1_Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.doc by Jijun Yang (5119103)

Transect in parts of California. by Qinghua Li (398885)

“…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”

ResNeXt101 training and results. by Subathra Gunasekaran (19492680)

“…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”

Architecture of ConvNet. by Subathra Gunasekaran (19492680)

“…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”

Comparison of state-of-the-art method. by Subathra Gunasekaran (19492680)

“…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”

Proposed ResNeXt101 operational flow. by Subathra Gunasekaran (19492680)

“…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”

Data_Sheet_1_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived paramete... by Kuo-Yang Huang (4470730)

Internal architecture of the SPAM-XAI model. by Mohd Mustaqeem (19106494)

“…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …”

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF by Jevitha Lobo (14368401)

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF by Jevitha Lobo (14368401)

Image 2_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png by Min Liang (363007)

“…</p>Conclusions<p>This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.…”