Intelligence-based medicine最新文献

{"title":"Optimizing melanoma diagnosis: A hybrid deep learning and quantum computing approach for enhanced lesion classification","authors":"Maria Frasca ,&nbsp;Ilaria Cutica ,&nbsp;Gabriella Pravettoni ,&nbsp;Davide La Torre","doi":"10.1016/j.ibmed.2025.100264","DOIUrl":"10.1016/j.ibmed.2025.100264","url":null,"abstract":"<div><div>Melanoma is one of the most aggressive forms of skin cancer, necessitating advanced diagnostic tools to improve early detection. This study presents a novel AI-driven approach that combines deep neural networks with quantum computing techniques for enhanced lesion classification. Specifically, we employ a U-Net model for segmentation and a hybrid Convolutional Neural Network - Quantum Neural Network (CNN-QNN) for classification. Our approach achieves a precision of 99.67 %, recall of 99.67 %, and an overall accuracy of 99.35 % on the HAM10000 dataset. Additionally, we report a sensitivity of 99.4 %, a specificity of 99.2 %, and a macro F1-score of 99.5 %, significantly surpassing traditional CNN-based classifiers. This hybrid model outperforms conventional deep learning approaches, demonstrating its potential for aiding dermatologists in clinical decision-making. A comparative analysis with state-of-the-art models further validates the effectiveness of our method.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural network based automatic detection of motor evoked potentials","authors":"Bethel Osuagwu ,&nbsp;Hongli Huang ,&nbsp;Emily L. McNicol ,&nbsp;Vellaisamy A.L. Roy ,&nbsp;Aleksandra Vučkovič","doi":"10.1016/j.ibmed.2025.100295","DOIUrl":"10.1016/j.ibmed.2025.100295","url":null,"abstract":"<div><h3>Introduction</h3><div>Motor evoked potentials (MEP) are detected using various methods that determine signal changepoints. The current detection methods perform well given a high signal to noise ratio. However, performance can diminish with artefact such as those arising due to poor signal quality and unwanted electrical potentials. Part of the problem is likely because the methods ignore the morphology of a signal making it impossible to differentiate noise from MEPs.</div></div><div><h3>Methods</h3><div>For the first time, we investigated a new detection method able to learn MEP morphology using artificial neural networks. To build an MEP detection model, we trained deep neural networks with architectures based on combined CNN and LSTM or self-attention mechanism, using sample MEP data recorded from able-bodied individuals. The MEP detection capability of the models was compared with that of a changepoint based detection method.</div></div><div><h3>Results</h3><div>Our models reached test accuracy of up to 89.7 ± 1.5 % on average. In a real-world setting evaluation, our models achieved average detection accuracy of up to 94.7 ± 1.2 %, compared with 76.4 ± 5.3 % for the standard changepoint detection method (p = 0.004).</div></div><div><h3>Conclusion</h3><div>Artificial neural network models can be used for improved automated detection of MEPs.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100295"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced X-ray image classification for pneumonia detection using deep learning based CBAM and SE mechanisms","authors":"Saiprasad Potharaju ,&nbsp;Swapnali N. Tambe ,&nbsp;Kishore Dasari ,&nbsp;N. Srikanth ,&nbsp;Rampay Venkatarao ,&nbsp;Sagar Tambe","doi":"10.1016/j.ibmed.2025.100299","DOIUrl":"10.1016/j.ibmed.2025.100299","url":null,"abstract":"<div><h3>Problem considered</h3><div>Pneumonia, a global health concern, remains a significant cause of morbidity and mortality, particularly in children under five and the elderly. Diagnostic challenges are pronounced in resource-limited settings, where expertise in radiological interpretation is scarce. X-ray imaging, a common diagnostic tool, often fails to provide accurate results without expert analysis. This gap in timely and precise diagnosis leads to delayed treatments and worsening patient outcomes. The emergence of antibiotic-resistant strains further emphasizes the urgency for innovative diagnostic solutions.</div></div><div><h3>Methods</h3><div>This research integrates advanced attention mechanisms into convolutional neural networks (CNNs) to enhance pneumonia detection from X-ray images. Utilizing a dataset of 5816 X-rays, preprocessing steps included normalization and data augmentation to improve robustness. The baseline CNN model was augmented with Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) networks, which prioritize critical image regions and recalibrate feature channels. Comparative evaluations were conducted using ResNet50 combined with CBAM.</div></div><div><h3>Results</h3><div>The CBAM-enhanced CNN achieved 98.6 % accuracy, improving upon the baseline CNN's 92.08 %, with a sensitivity of 98.3 % and specificity of 97.9 %. The SE-integrated CNN followed with 96.25 % accuracy, demonstrating superior feature recalibration. ResNet50 with CBAM attained 93.32 % accuracy. Compared to standard CNN models, these models exhibited reduced overfitting, improved generalization, and enhanced feature extraction. The proposed approach ensures a higher precision rate in detecting pneumonia from X-ray images. The model is designed for real-world clinical applications, particularly in low-resource healthcare settings. A lightweight, user-friendly web application was developed to assist radiologists and general practitioners in automated pneumonia detection, reducing reliance on expert interpretation.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the prevalence of cardiovascular diseases using machine learning algorithms","authors":"Bernada E. Sianga ,&nbsp;Maurice C. Mbago ,&nbsp;Amina S. Msengwa","doi":"10.1016/j.ibmed.2025.100199","DOIUrl":"10.1016/j.ibmed.2025.100199","url":null,"abstract":"<div><div>Cardiovascular Diseases (CVDs) are the major cause of morbidity, disability, and mortality worldwide and are the most life-threatening diseases. Early detection and appropriate action can significantly reduce the effects and complications of CVD. Prediction of the likelihood that an individual can develop CVD adverse outcomes is essential. Machine learning methods are used to predict the risk of CVD incidences. Optimal model parameters were obtained using the grid search and randomized search methods. A hyperparameter tuning method with the highest accuracy was used to find the optimal parameters for the six algorithms used in this study. Two experiments were deployed: the first was training and testing the CVD dataset using hyperparameterized ML algorithms excluding geographical features, and the second included geographical features. The geographical features are air humidity, temperature and education status of a location. The performances of the two experiments were compared using classification metrics. The findings revealed that the performance of the second experiment outperformed the first experiment. XGBoost achieved the highest accuracy of 95.24 %, followed by the decision tree 93.87 % and support vector machine 92.87 % when geographical features were included (second experiment). Including geographical risk factors in predicting CVD is crucial as they contribute to the probability of developing CVD incidences.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A neonatal sepsis prediction algorithm using electronic medical record data from Mbarara Regional Referral Hospital","authors":"Peace Ezeobi Dennis ,&nbsp;Angella Musiimenta ,&nbsp;William Wasswa ,&nbsp;Stella Kyoyagala","doi":"10.1016/j.ibmed.2025.100198","DOIUrl":"10.1016/j.ibmed.2025.100198","url":null,"abstract":"<div><h3>Introduction</h3><div>Neonatal sepsis is a global challenge that contributes significantly to neonatal morbidity and mortality. The current diagnostic methods depend on conventional culture methods, a procedure that takes time and leads to delays in making timely treatment decisions. This study proposes a machine learning algorithm utilizing electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) to enhance early detection and treatment of neonatal sepsis.</div></div><div><h3>Methods</h3><div>We performed a retrospective study on a dataset of neonates hospitalized for at least 48 h in the Neonatal Intensive Care Unit (NICU) at MRRH between October 2015 to September 2019 who received at least one sepsis evaluation. 482 records of neonates met the inclusion criteria and the dataset comprises 38 neonatal sepsis screening parameters. The study considered two outcomes for sepsis evaluations: culture-positive if a blood culture was positive, and clinically positive if cultures were negative but antibiotics were administered for at least 120 h. We implemented k-fold cross-validation with k set to 10 to guarantee robust training and testing of the models. Seven machine learning models were trained to classify inputs as sepsis positive or negative, and their performance was compared with physician diagnoses.</div></div><div><h3>Results</h3><div>The results of this study show that the proposed algorithm, combining maternal risk factors, neonatal clinical signs, and laboratory tests (the algorithm demonstrated a sensitivity and specificity of at least 95 %) outperformed the physician diagnosis (Sensitivity = 89 %, Specificity = 11 %). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98 %) performed better than the other models.</div></div><div><h3>Conclusions</h3><div>The study shows that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests can help improve the prediction of neonatal sepsis. Further research is warranted to assess the potential performance improvements and clinical efficacy in a prospective trial.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based approach to diagnose lung cancer using CT-scan images","authors":"Mohammad Q. Shatnawi,&nbsp;Qusai Abuein,&nbsp;Romesaa Al-Quraan","doi":"10.1016/j.ibmed.2024.100188","DOIUrl":"10.1016/j.ibmed.2024.100188","url":null,"abstract":"<div><div>The work in this research focuses on the automatic classification and prediction of lung cancer using computed tomography (CT) scans, employing Deep Learning (DL) strategies, specifically Enhanced Convolutional Neural Networks (CNNs), to enable rapid and accurate image analysis. This research designed and developed pre-trained models, including ConvNeXtSmall, VGG16, ResNet50, InceptionV3, and EfficientNetB0, to classify lung cancer. The dataset was divided into four classes, consisting of 338 images of adenocarcinoma, 187 images of large cell carcinoma, 260 images of squamous cell carcinoma, and 215 normal images. Notably, The Enhanced CNN model achieved an unprecedented testing accuracy of 100 %, outperforming all other models, which included ConvNeXt at 87 %, VGG16 at 99 %, ResNet50 at 94.5 %, InceptionV3 at 76.9 %, and EfficientNetB0 at 97.9 %. The study of this research is considered the first one that hits 100 % testing accuracy with an Enhanced CNN, demonstrating significant advancements in lung cancer detection through the application of sophisticated image enhancement techniques and innovative model architectures. This highlights the potential of Enhanced CNN models in transforming lung cancer diagnostics and emphasizes the importance of integrating advanced image processing techniques into clinical practice.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting patient no-shows using machine learning: A comprehensive review and future research agenda","authors":"Khaled M. Toffaha ,&nbsp;Mecit Can Emre Simsekler ,&nbsp;Mohammed Atif Omar ,&nbsp;Imad ElKebbi","doi":"10.1016/j.ibmed.2025.100229","DOIUrl":"10.1016/j.ibmed.2025.100229","url":null,"abstract":"<div><div>Patient no-shows for scheduled medical appointments pose significant challenges to healthcare systems, resulting in wasted resources, increased costs, and disrupted continuity of care. This comprehensive review examines state-of-the-art machine learning (ML) approaches for predicting patient no-shows in outpatient settings, analyzing 52 publications from 2010 to 2025.</div><div>The study reveals significant advancements in the field, with Logistic Regression (LR) as the most commonly used model in 68% of the studies. Tree-based models, ensemble methods, and deep learning techniques have gained traction in recent years, reflecting the field’s evolution. The best-performing models achieved Area Under the Curve (AUC) scores between 0.75 and 0.95, with accuracy ranging from 52% to 99.44%. Methodologically, researchers addressed common challenges such as class imbalance using various sampling techniques and employed a wide range of feature selection methods to improve model efficiency. The review also highlighted the importance of considering temporal factors and the context-dependent nature of no-show behavior across different healthcare settings.</div><div>Using the ITPOSMO framework (Information, Technology, Processes, Objectives, Staffing, Management, and Other Resources), the study identified several gaps in current ML approaches. Key challenges include data quality and completeness, model interpretability, and integration with existing healthcare systems. Future research directions include improving data collection methods, incorporating organizational factors, ensuring ethical implementation, and developing standardized approaches for handling data imbalance. The review also suggests exploring new data sources, utilizing ML algorithms to analyze patient behavior patterns, and using transfer learning techniques to adapt models across different healthcare facilities.</div><div>By addressing these challenges, healthcare providers can leverage ML to improve resource allocation, enhance patient care quality, and advance predictive analytics in healthcare. This comprehensive review underscores the potential of ML in predicting no-shows while acknowledging the complexities and challenges in its practical implementation.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid vision transformer and Xception model for reliable CT-based ovarian neoplasms diagnosis","authors":"Eman Hussein Alshdaifat ,&nbsp;Hasan Gharaibeh ,&nbsp;Amer Mahmoud Sindiani ,&nbsp;Rola Madain ,&nbsp;Asma'a Mohammad Al-Mnayyis ,&nbsp;Hamad Yahia Abu Mhanna ,&nbsp;Rawan Eimad Almahmoud ,&nbsp;Hanan Fawaz Akhdar ,&nbsp;Mohammad Amin ,&nbsp;Ahmad Nasayreh ,&nbsp;Raneem Hamad","doi":"10.1016/j.ibmed.2025.100227","DOIUrl":"10.1016/j.ibmed.2025.100227","url":null,"abstract":"<div><div>Ovarian cancer is a major global health concern, characterized by high mortality rates and a lack of accurate diagnostic methods. Rapid and accurate detection of ovarian cancer is essential to improve patient outcomes and formulate appropriate treatment protocols. Medical imaging methods are essential for identifying ovarian cancer; however, achieving accurate diagnosis remains a challenge. This paper presents a robust methodology for ovarian cancer detection, including the identification and classification of benign and malignant tumors, using the Xception_ViT model. This hybrid approach was chosen because it combines the advantages of traditional CNN-based models (such as Xception) with the capabilities of modern Transformers-based models (such as ViT). This combination allows the model to take advantage of Xception, which extracts features from images. The Vision Transformer (ViT) model is then used to identify connections between diverse visual elements, enhancing the model's understanding of complex components. A Multi-Layer Perceptron (MLP) layer is finally integrated with the proposed model for image classification. The effectiveness of the model is evaluated using three computed tomography (CT) image datasets from King Abdullah University Hospital (KAUH) in Jordan. The first dataset consists of the ovarian cancer computed tomography dataset (KAUH-OCCTD), the second is the benign ovarian tumors dataset (KAUH-BOTD), and the third is the malignant ovarian tumors dataset (KAUH-MOTD). The three datasets collected from 500 women are characterized by their diversity in ovarian tumor classification and are the first of their kind collected in Jordan. The proposed model Xception_ViT achieved an accuracy of 98.09 % in identifying ovarian cancer on the KAUH-OCCTD dataset, and an accuracy of 96.05 % and 98.73 % on the KAUH-BOTD and KAUH-MOTD datasets, respectively, in distinguishing between benign and malignant ovarian tumors. The proposed model outperformed the pre-trained models on all three datasets. The results demonstrate that the proposed model can classify ovarian tumors. This method could also greatly enhance the efficiency of novice radiologists in evaluating ovarian malignancies and assist gynecologists in providing improved treatment alternatives for these individuals.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases","authors":"Timothy Albiges,&nbsp;Zoheir Sabeur,&nbsp;Banafshe Arbab-Zavar","doi":"10.1016/j.ibmed.2025.100217","DOIUrl":"10.1016/j.ibmed.2025.100217","url":null,"abstract":"<div><div>Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life.</div><div>Auscultation lung sound analysis has emerged as a valuable, non-invasive, and cost-effective remote diagnostic tool of the future for respiratory conditions such as COPD. This research paper introduces a novel machine learning-based approach for classifying multiple COPD severities through the analysis of lung sound data streams. Leveraging two open datasets with diverse acoustic characteristics and clinical manifestations, the research study involves the transformation and decomposition of lung sound data matrices into their eigenspace representation in order to capture key features for machine learning and detection. Early eigenvalue spectra analyses were also performed to discover their distinct manifestations under the multiple established COPD severities. This has led us into projecting our experimental data matrices into their eigenspace with the use of the manifested data features prior to the machine learning process. This was followed by various methods of machine classification of COPD severities successfully. Support Vector Classifiers, Logistic Regression, Random Forests and Naive Bayes Classifiers were deployed. Systematic classifier performance metrics were also adopted; they showed early promising classification accuracies beyond 75 % for distinguishing COPD severities.</div><div>This research benchmark contributes to computer-aided medical diagnosis and supports the integration of auscultation lung sound analyses into COPD assessment protocols for individualised patient care and treatment. Future work involves the acquisition of larger volumes of lung sound data while also exploring multi-modal sensing of COPD patients for heterogeneous data fusion to advance COPD severity classification performance.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting maternal health risk using PCA-enhanced XGBoost and SMOTE-ENN for improved healthcare outcomes","authors":"Rahmatul Kabir Rasel Sarker ,&nbsp;Sadman Hafij ,&nbsp;Md Adib Yasir ,&nbsp;Md Assaduzzaman ,&nbsp;Md Monir Hossain Shimul ,&nbsp;Md Kamrul Hossain","doi":"10.1016/j.ibmed.2025.100300","DOIUrl":"10.1016/j.ibmed.2025.100300","url":null,"abstract":"<div><h3>Background</h3><div>Maternal health remains a global priority, especially in low-resource settings where timely risk identification is critical. Traditional machine learning models often suffer from poor generalizability, data imbalance, and computational inefficiencies. This study proposes an enhanced predictive model combining SMOTE-ENN data balancing and Principal Component Analysis (PCA) with XGBoost to improve maternal risk classification accuracy using minimal, easily collectible clinical features.</div></div><div><h3>Methods</h3><div>The dataset of 1014 maternal health records comprising seven physiological features was sourced from a public repository. Preprocessing involved standardization, label encoding, and class balancing using SMOTE-ENN. PCA was applied for dimensionality reduction to enhance computational performance and reduce overfitting. Several machine learning classifiers including Decision Tree, Random Forest, LightGBM, Gradient Boosting, and SVM were evaluated, with XGBoost selected as the final model. Performance metrics included accuracy, precision, recall, F1-score, ROC-AUC, and 10-fold cross-validation.</div></div><div><h3>Results</h3><div>The PCA-enhanced XGBoost model achieved the highest accuracy (97.73 %), precision (98 %), recall (98 %), and F1-score (98 %). It outperformed all other models, particularly in identifying high-risk cases with minimal false negatives. Cross-validation confirmed the model's robustness (mean accuracy: 98.39 %), and ROC-AUC scores exceeded 0.998 for all classes, indicating near-perfect classification performance.</div></div><div><h3>Conclusion</h3><div>This study validates a maternal health risk prediction model that is scalable for use in resource-constrained environments and interpretable within the limitations of the selected dimensionality-reduction approach. Its simplicity, high accuracy, and generalizability make it a promising tool for early clinical decision-making and intervention.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}