Informatics in Medicine Unlocked最新文献

{"title":"Corrigendum to “Early detection of coronary heart disease using ensemble techniques” [Inform Med Unlocked 26 (2021) 100655]","authors":"Vardhan Shorewala , Shivam Shorewala","doi":"10.1016/j.imu.2024.101598","DOIUrl":"10.1016/j.imu.2024.101598","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101598"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178836","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":"Large language models aided patient progression documentation according to the ICD standard","authors":"Nuria Lebeña ,&nbsp;Arantza Casillas ,&nbsp;Alicia Pérez","doi":"10.1016/j.imu.2025.101637","DOIUrl":"10.1016/j.imu.2025.101637","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Healthcare documentation processing is becoming more and more efficient and effective as a result of advances in machine learning and natural language processing (NLP). One challenge in clinical practice is the early detection of future patient potential diagnoses, which is crucial for preventive medicine. Estimating the potential future diagnoses, helps to speed up the management of Electronic Health Records (EHRs) and opens a path towards clinical prevention. It is a challenging task, as there are thousands of possible diseases, and, in general, there is limited data available to train systems due to privacy concerns.</div><div>The objective of his study is to infer future probable diagnoses given patients diagnosis history. In previous works, this task has been carried out using structured data, such as, ICD-coded diagnoses, overlooking unstructured textual information in EHRs. Unlike traditional methods, this study aims to enhance next-diagnosis prediction by integrating patient diagnosis information codified according to the International Classification of Diseases (ICD) with unstructured clinical text.</div></div><div><h3>Methods:</h3><div>We propose a multi-faceted model that integrates structured ICD-encoded patient histories with unstructured EHR text for future diagnosis prediction. Our approach consists of (1) a sequential model trained on structured diagnosis timelines, (2) a Clinical Longformer-based model trained on unstructured EHRs, and (3) an ensemble strategy to combine predictions from both components.</div></div><div><h3>Results:</h3><div>Our proposed ensemble strategy significantly outperforms current state-of-the-art approaches in predicting future diagnoses, achieving a Precision@5 of 72.34% and a Precision@20 of 77.49%. Additionally, it showed high robustness and reliability across different demographic groups and a varying scope of medical history.</div></div><div><h3>Conclusion:</h3><div>This research demonstrates that the integration of structured ICD diagnoses timelines with unstructured EHRs achieves improved results compared to just using structured diagnosis timelines. Notably, the proposed model also maintained high accuracy even with a short-term history of diagnoses.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101637"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739798","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":"Examining the association between genetic polymorphisms and osteoporosis among post-menopausal women: a systematic review","authors":"Zainab Alhalwachi ,&nbsp;Mira Mousa ,&nbsp;Salsabeel Juneidi ,&nbsp;Gabriela Restrepo-Rodas ,&nbsp;Spyridon Karras ,&nbsp;Habiba Alsafar ,&nbsp;Fatme Al Anouti","doi":"10.1016/j.imu.2025.101652","DOIUrl":"10.1016/j.imu.2025.101652","url":null,"abstract":"<div><h3>Purpose</h3><div>Postmenopausal osteoporosis (PMOP) is the most prevalent metabolic bone disease among women, characterized by significant bone density loss and increased fracture risk. With a genetic component, a systematic review was conducted on the association between genetic polymorphisms and PMOP risk.</div></div><div><h3>Methods</h3><div>A comprehensive review of PubMed literature examined genetic polymorphisms linked to PMOP risk. The primary outcome was to identify the most frequently studied genes linked to PMOP. The secondary outcome was to perform a meta-analysis on the top genetic markers to assess their overall association with PMOP risk.</div></div><div><h3>Results</h3><div>Six genes, accounting for 55.08 % of all studies, were strongly associated with PMOP. Of these, the <em>VDR</em> gene was featured in 35 articles (18.72 % of studies), TNFRSF11B in 23 (12.30 %), <em>ESR1</em> in 18 (9.63 %), <em>COL1A1</em> in 12 (6.42 %), <em>MTHFR</em> in 8 (4.27 %), and TGFb1 in 7 (3.74 %). Meta-analysis showed five markers significantly associated with PMOP: SNP rs1544410 (OR_G: 0.74 (0.59, 0.92)), SNP rs11568820 (OR_G: 1.40 (1.03, 1.91)), and SNP rs2228570 (OR_T: 1.39 (1.12, 1.73)) in the <em>VDR</em> gene; and PvuII variant (OR_P: 0.80 (0.67, 0.96)) in the <em>ESR1</em> gene.</div></div><div><h3>Conclusion</h3><div>This review strengthens the importance of conducting a robust, multi-ethnic, large cohort study with functional analysis to corroborate the findings of the six key genes associated with PMOP. Replicating these findings in larger and more diverse datasets is crucial to validate their biological relevance and potential clinical application.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101652"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070359","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":"Multi-model deep learning approach for the classification of kidney diseases using medical images","authors":"Waleed Obaid ,&nbsp;Abir Hussain ,&nbsp;Tamer Rabie ,&nbsp;Dhafar Hamed Abd ,&nbsp;Wathiq Mansoor","doi":"10.1016/j.imu.2025.101663","DOIUrl":"10.1016/j.imu.2025.101663","url":null,"abstract":"<div><div>Renal impairment poses a risk across all ages. With the global nephrologist shortage, the rising public health concerns over kidney failure, and advancements in technology, there is a growing need for an AI system capable of identifying kidney anomalies automatically. Chronic kidney disease is marked by a gradual failure in kidney function due to various factors, such as stones, cysts, and tumors. Chronic kidney disease often presents without noticeable symptoms initially, leading to cases remaining untreated until advanced stages. Tumors, which are dense tissue masses, can directly harm organs, including glands and spinal cells. Kidney stone disease, or urolithiasis, occurs when many solids accumulate in the urinary tract, leading to stone formation. This research paper leveraged a deep learning approach to address the worldwide shortage of urologists by facilitating the detection of kidney diseases. A novel deep learning technique is proposed using Darknet53 for the classification of kidney diseases using a large dataset gathered from five resources. The total number of images is 27,145 scans of the entire abdomen and urogram, focusing on common kidney conditions, including stones, cysts, and tumors. The data was grouped into four classes: normal, cyst, tumor, and stone. The proposed technique involves the use of 16 deep-learning models to obtain enhanced performance based on accuracy, recall, specificity, and precision, offering new potential for detecting kidney abnormalities. Model performance was evaluated, achieving 99.69 %, 0.31 %, 99.66 %, 99.88 %, 99.77 %, 0.12 %, 99.71 %, 99.60 %, and 99.17 % for accuracy, error, recall, specificity, precision, false positive rate, F1_score, Matthews Correlation Coefficient, and Kappa, respectively. Our simulation results using the Fuzzy Decision by Opinion Score Method indicated that the Darknet53 generated the best results for detecting kidney abnormalities.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101663"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298316","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":"A scoping review of the use of artificial intelligence models in automated OCT analysis and prediction of treatment outcomes in diabetic macular oedema","authors":"Mohaimen Al-Zubaidy ,&nbsp;Agnieszka Stankiewicz ,&nbsp;Matthew Anderson ,&nbsp;Jordan Reed ,&nbsp;Veronica Corona ,&nbsp;Rebecca Pope ,&nbsp;Boguslaw Obara ,&nbsp;Maged S. Habib ,&nbsp;David H. Steel","doi":"10.1016/j.imu.2025.101676","DOIUrl":"10.1016/j.imu.2025.101676","url":null,"abstract":"<div><h3>Objective</h3><div>This review aims to identify gaps and provide direction for future research examining the use of artificial intelligence (AI) and optical coherence tomography (OCT) in the investigation and management of diabetic macular oedema (DMO)<strong>.</strong></div></div><div><h3>Methods</h3><div>A comprehensive literature search was conducted using MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Database, and the Web of Science. The search focused on AI applications in DMO diagnosis, grading, and outcome prediction, and adhered to a predefined protocol following the Cochrane Methodology for Scoping Reviews.</div></div><div><h3>Results</h3><div>Following screening 40 studies were included for review. The review highlighted significant advancements in the use of AI for DMO, particularly in diagnosis and biomarker detection. AI models demonstrated high accuracy in distinguishing DMO from other retinal conditions and in segmenting key DMO biomarkers.</div></div><div><h3>Conclusion</h3><div>The review concludes that future research should focus on developing robust prognostic and treatment prediction models, improving external validation and standardising performance metrics. Addressing these challenges is essential for optimising the integration of AI into DMO management, ultimately improving patient outcomes and reducing vision impairment.</div></div><div><h3>Significance</h3><div>This review underscores AI's potential to transform DMO management, a leading cause of vision impairment in diabetes. The identified gaps and future research directions offer valuable insights for researchers and practitioners, with the potential to significantly improve patient care and healthcare efficiency.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101676"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749341","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":"In-context learning for label-efficient cancer image classification in oncology","authors":"Mobina Shrestha ,&nbsp;Bishwas Mandal ,&nbsp;Vishal Mandal ,&nbsp;Asis Shrestha ,&nbsp;Amir Babu Shrestha","doi":"10.1016/j.imu.2025.101683","DOIUrl":"10.1016/j.imu.2025.101683","url":null,"abstract":"<div><div>The application of artificial intelligence in oncology has been limited by its reliance on large, annotated datasets and the need for retraining models for domain-specific diagnostic tasks. Taking heed of these limitations, we investigated in-context learning as a pragmatic alternative to model retraining by allowing models to adapt to new diagnostic tasks using only a few labeled examples at inference, without the need for retraining. Using four vision-language models (VLMs) -- Paligemma, CLIP, ALIGN and GPT-4o, we evaluated the performance across three oncology datasets: MHIST, PatchCamelyon and HAM10000. To the best of our knowledge, this is the first study to compare the performance of multiple VLMs with in-context learning on different oncology classification tasks. Without any parameter updates, all models showed significant gains with few-shot prompting, with GPT-4o reaching an F1 score of 0.81 in binary classification and 0.60 in multi-class classification settings. While these results remain below the ceiling of fully fine-tuned systems, they highlight the potential of ICL to approximate task-specific behavior using only a handful of examples, reflecting how clinicians often reason from prior cases. Notably, open-source models like Paligemma and CLIP demonstrated competitive gains despite their smaller size, suggesting feasibility for deployment in computing constrained clinical environments. Overall, these findings highlight the potential of ICL as a practical solution in oncology, particularly for rare cancers and resource-limited contexts where fine-tuning is infeasible and annotated data is difficult to obtain.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101683"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887256","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":"Early detection and classification of diabetic retinopathy by transfer learning of NASNet-large and ResNet-50 convolutional neural networks","authors":"Sreebhadra Vallukappully ,&nbsp;Ian van der Linde ,&nbsp;Ashim Chakraborty","doi":"10.1016/j.imu.2025.101688","DOIUrl":"10.1016/j.imu.2025.101688","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is a progressive eye disease that affects those with long-term diabetes. It can lead to irreversible blindness if not detected and treated early. Early detection is challenging as changes to the retina are initially subtle. A number of computational models have been proposed to detect DR in fundus images, including in its early stages. Here, a novel transfer learning approach is proposed using the NASNet-Large and ResNet-50 convolutional neural networks. Image pre-processing steps are tested combinatorically. Class imbalance is addressed with oversampling and data augmentation to give trustworthy performance metrics. The models give impressive detection rates using a standard dataset containing expert-labelled DR fundus images (APTOS 2019), with the best performing models giving accuracy in classifying unseen images exceeding 0.96 (F1 score 0.97) for Early-stage DR detection (no DR vs mild and moderate), and over 0.91 accuracy (F1 score 0.91) for Multi-stage classification (no DR, mild, moderate, severe, and proliferative). This work highlights the potential of combining the transfer learning of state-of-the-art deep learning models with classical image processing for effective DR detection and classification.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101688"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010545","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":"FedDeepInsight—A privacy-first federated learning architecture for medical data","authors":"Allan G. Duah ,&nbsp;Roland V. Bumbuc ,&nbsp;H. Ibrahim Korkmaz ,&nbsp;Rory Wilding ,&nbsp;Vivek M. Sheraton","doi":"10.1016/j.imu.2025.101691","DOIUrl":"10.1016/j.imu.2025.101691","url":null,"abstract":"<div><div>Medical data, hospital patient-specific data, are highly sensitive to privacy and are essential for research in the biomedical field. Although there are many new approaches to creating databases that ensure data must be FAIR and GDPR compliant, these approaches require the intervention of secured data handlers. To address this gap, this study investigates and designs a standardized Federated Learning (FL) architecture for medical data. Specifically, we examine traditional and novel methods for preprocessing, handling, and utilizing such data in FL. We develop “FedDeepInsight”, a novel data transformation framework that enables tabular data augmentation and transformation into image data prior to neural network training and FL. Additionally, we analyze how the type of dataset influences the performance of federated learning algorithms and machine learning models in terms of accuracy and efficiency. Our results indicate that FedAvg is the most reliable aggregation algorithm, providing superior accuracy, stability, and convergence, and FedYogi is also viable with well-tuned hyperparameters. For privacy protection, we recommend Differential Privacy (DP) with calibrated noise multipliers and initial upper and lower bounds for stability. Ultimately, we emerge as a promising solution for secure, privacy-preserving federation learning in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101691"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059881","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":"Using Fuzzy C-Means clustering and PCA in public health: A machine learning approach to combat CVD and obesity","authors":"Gamal Saad Mohamed Khamis ,&nbsp;Nasser S. Alqahtani ,&nbsp;Sultan Munadi Alanazi ,&nbsp;Mohammed Muharrab Alruwaili ,&nbsp;Mariam Shabram Alenazi ,&nbsp;Maneaf A. Alrawaili","doi":"10.1016/j.imu.2025.101666","DOIUrl":"10.1016/j.imu.2025.101666","url":null,"abstract":"<div><div>This study introduces a novel framework that integrates principal component analysis (PCA) with fuzzy c-means (FCM) clustering to enhance the analysis of high-dimensional health data, specifically targeting the identification of at-risk groups for cardiovascular disease (CVD) and obesity. This unique approach, which has not been previously explored in public health, promises to provide new insights and solutions to these pressing health issues.</div><div>The proposed PCA-FCM model was applied to a dataset comprising more than 20 health variables from a population sample aged 18–75 years. The analysis identified four distinct clusters, each showing unique risk patterns. For instance, Cluster One (mean age, 29) showed elevated body mass index (BMI) (mean, 33.7 kg/m²), high waist circumference (113 cm), and signs of insulin resistance (FBS, 133 mg/dL; HOMA-IR, 7.12). In contrast, Cluster Two (mean age, 61) exhibited the highest systolic blood pressure (SBP, 143 mmHg), elevated LDL cholesterol (4.27 mmol/L), and triglycerides (2.59 mmol/L), indicating advanced metabolic syndrome. Cluster Three (mean age, 51) presented a healthier metabolic profile with lower HOMA-IR (3.74), normal SBP (127 mmHg), and balanced lipid levels (HDL, 1.36 mmol/L). Cluster Four (mean age, 43) showed elevated SBP (134 mmHg), BMI (32.1 kg/m²), and HOMA-IR (6.05), suggesting a latent risk group.</div><div>PCA identified waist circumference, visceral fat, LDL/HDL ratio, non-HDL cholesterol, and waist-to-height ratio as the most influential variables contributing to cluster separation, with loadings above 0.70 on the first two principal components. Meanwhile, exercise, height, family history, and HDL had loadings below 0.30, indicating minimal influence on cluster formation.</div><div>The model evaluation supported the selection of the four-cluster solution, with a Silhouette Score of 0.62 and Between-Cluster Variation accounting for 64 % of the total variance, signifying well-defined and cohesive clusters.</div><div>Although this framework enhances clustering precision and uncovers clinically actionable patterns, challenges such as health data privacy concerns, clinicians’ difficulty in interpreting PCA results, validating model generalizability across diverse populations, and technical resource limitations in low-resource settings must be addressed for successful implementation in real-world healthcare systems. Future research should incorporate longitudinal data and explore integration with advanced models, such as deep learning, to improve predictive accuracy and adaptability in real-time clinical environments.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101666"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597124","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":"Revolutionizing heart attack prognosis: Introducing an innovative regression model for prediction","authors":"Hanaa Albanna ,&nbsp;Madhav Raj Theeng Tamang ,&nbsp;Chandan Patel ,&nbsp;Mhd Saeed Sharif","doi":"10.1016/j.imu.2025.101664","DOIUrl":"10.1016/j.imu.2025.101664","url":null,"abstract":"<div><h3>Objective:</h3><div>Heart attack prediction using machine learning is crucial for preemptive action and personalized healthcare. This research aims to predict heart attacks by employing machine learning in healthcare using a diverse range of patient data-including demographic, lifestyle, and physiological factors, which helps to create robust and generalizable predictions. Besides this, various models that balance accuracy with interpretability have been presented, emphasizing early detection and proactive intervention. It is expected that this cross-disciplinary approach will underline the role of machine learning in the mitigation of the heart disease burden and optimization of resources spent on healthcare.</div></div><div><h3>Methods:</h3><div>This study explores the application of machine learning techniques for predicting heart attack risk using structured clinical data. A range of classification models — Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) — were selected based on their proven effectiveness in prior healthcare prediction studies and their balance between accuracy and interpretability. The methodology involved comprehensive data preprocessing, class imbalance handling, and hyperparameter tuning to optimize model performance. Performance metrics included Accuracy, Precision, Recall, F1-score, and AUC-ROC. Exploratory Data Analysis (EDA) was conducted to assess the role of variables such as BMI, age, and glucose levels in predicting stroke, a proxy used for heart attack due to dataset limitations.</div></div><div><h3>Results:</h3><div>The SVM and LR models achieved the highest accuracy (95.08%), followed by RF (94.86%) and DT (91.46%). Despite high accuracy, key challenges were observed:</div><div>Class Imbalance: Only 249 cases in the dataset represented positive stroke outcomes, resulting in poor recall for minority class predictions. This reduced the model’s sensitivity to actual stroke cases, a significant limitation in clinical scenarios where false negatives can be life-threatening.</div><div>Data-Label Inconsistency: Although the study is framed as predicting heart attacks, the dataset pertains to stroke prediction. This misalignment creates confusion in the clinical relevance of the findings and weakens the generalizability of the models for heart attack risk assessment.</div><div>Lack of Model Interpretability in Practice: Though LIME and SHAP were cited as tools to ensure model transparency, they were not implemented or evaluated. This limits clinicians’ trust in the model’s predictions—an essential factor for real-world adoption.</div></div><div><h3>Conclusion:</h3><div>This research shows how machine learning can play a meaningful role in improving how we predict heart attacks and ultimately help improve patient care. The results demonstrated that even well-known models like Support Vector Machine and Logistic Regression can perform very well when applied to s","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101664"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633341","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}