Area Under The ROC Curve Research Articles

Choroid plexus volume and association with migraine Pathophysiology.

Choroid plexus volume and association with migraine Pathophysiology.

External validation of a clinical risk score for the presence of cardiovascular autonomic neuropathy in type 1 diabetes.

External validation of a clinical risk score for the presence of cardiovascular autonomic neuropathy in type 1 diabetes.

Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study

Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study

Screening for depressive disorders: Validation of the Patient Health Questionnaire for Adolescents (PHQ-A) in a population-based multi-ethnic Asian sample.

Screening for depressive disorders: Validation of the Patient Health Questionnaire for Adolescents (PHQ-A) in a population-based multi-ethnic Asian sample.

Validation of the Node Reporting and Data System (Node-RADS) for standardized CT evaluation of regional lymph nodes in esophageal squamous cell carcinoma patients.

The accurate identification of positive lymph nodes in esophageal squamous cell carcinoma (ESCC) influences patient risk assessment and treatment decisions, but there is no standardized approach for radiological evaluation. The aim of this study was to verify the diagnostic performance of the new Node Reporting and Data System 1.0 (Node-RADS) in the assessment of lymph node metastasis in patients with ESCC, as verified by final histopathology. Node-RADS is a scoring system composed of different criteria for evaluating lymph node metastasis, with scores ranging from 1 to 5, corresponding to the degree of suspicion of lymph node involvement. In this single-center study, Node-RADS was used to retrospectively evaluate regional lymph nodes in 173 ESCC patients who underwent computed tomography (CT) before radical resection. In addition, the area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the Node-RADS score and individual criteria. Node-RADS outperformed the individual assessment criteria (AUC: 94.3%, sensitivity: 96.5%, specificity: 92.0%), with scores ≥ 3 indicating the maximum diagnostic effectiveness. The diagnostic efficiency of the highest Node-RADS score surpassed that of the short axis score (AUC: 94.3% vs. 81.9%, p < 0.001). Our results indicated that the best diagnostic cut-off points for the short axis, long axis and short axis/long axis ratio were 9 mm, 11 mm, and 0.74, respectively. Node-RADS has emerged as a practical, repetitive method for the early identification of high-risk metastatic lymph nodes, providing therapeutic guidance and predicting disease prognosis in ESCC patients. Question How does the Node Reporting and Data System 1.0 (Node-RADS) perform in the assessment of lymph node metastasis in patients with esophageal squamous cell carcinoma (ESCC)? Findings The maximum diagnostic efficiency was achieved with a Node-RADS score of ≥ 3. Clinical relevance The Node-RADS has improved diagnostic efficiency for distinguishing lymph node metastasis in patients with ESCC.

Enhancement of 30-day acute care event prediction model following outpatient chemotherapy using vital signs, lab results, and advanced machine learning: A comparative analysis.

e13586 Background: This study aimed to improve the predictive performance of acute care events within 30 days (ACE30) following outpatient chemotherapy administration. Previous work by Stein et al. (JOP, 2023), focused on initial chemo administration, utilized 9 demographic and therapeutic-related variables (e.g., age, race, BMI, cancer type, etc.), and achieved a positive predictive value (PPV) of 0.23 and area under the ROC curve (AUC) of 0.65. Our subsequent research (He et al., ASCO 2024) explored advanced machine learning (ML) models with resampling strategies using same variables, which yielded only minimal improvements in PPV and AUC. This study using all chemo administrations sought to enhance ACE30 prediction by incorporating additional vital signs and laboratory measurements into the existing predictor set. Methods: Data from 190,629 chemotherapy administrations delivered at Orlando Health between February 2012 to April 2021 were divided into training (133,440) and validation (57,189) sets. Two experiments were conducted: Experiment I incorporated pre-chemotherapy vital signs (e.g., temperature, blood pressure, and heart rate) with previous predictors. Experiment II added pre-chemotherapy laboratory measurements (WBC, HGB, Na, Ca, K, and Mg), and previous predictors. Three modeling approaches were employed: L1-penalized logistic regression, XGBoost (a nonlinear tree-based algorithm), and artificial neural networks (ANN). Using the output predicted probabilities, the top 10% patients were identified as high risk, and the remaining were identified as low risk. Results: In the validation dataset, the ANN model demonstrated superior performance in Experiment I, achieving a PPV of 0.62 &amp; AUC of 0.79. Experiment II yielded comparable results with the ANN model (PPV: 0.56 &amp; AUC: 0.78). Of the 154 administrations identified as high risk in Experiment I, 96 (62%) had an ACE30. Conclusions: The model expansion significantly improved the predictive performance of ACE30 events following outpatient chemotherapy administration, with ANN models consistently outperforming other modelling approaches. In our prior models, administrations identified as high risk had an actual ACE30 only 23% of the time. The ANN model with additional vital signs and labs developed in this study identified actual acute care events 62% of the time. These findings suggest pre-chemotherapy vital signs and possibly pre-chemotherapy lab results are crucial predictors for acute care events. These results are potentially strong enough to suggest implementation into our clinical workflows to lower rate of emergency room visits and hospitalizations.

Exposure-response relationship of mycophenolic acid in pediatric lupus nephritis patients receiving multi-target therapy: An observational cohort study.

Exposure-response relationship of mycophenolic acid in pediatric lupus nephritis patients receiving multi-target therapy: An observational cohort study.

GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus.

GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus.

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

Risk factors and predictive nomogram for non-curative resection in patients with early gastric cancer treated with endoscopic submucosal dissection: a retrospective cohort study

IntroductionThe objective of this study was to determine independent clinicopathological factors that can predict submucosal invasion and non-curative resection (NCR) outcomes after endoscopic submucosal dissection (ESD) in patients with early gastric cancer (EGC).MethodsData were collected from consecutive patients who underwent gastric ESD at the First Affiliated Hospital of Ningbo University between 2016 and 2023. A retrospective analysis was conducted using the chi-squared test and logistic regression analysis. Multiple logistic regression analysis was applied to investigate factors independently predicting both submucosal invasion and NCR. These factors were used to construct predictive nomograms.ResultsA total of 511 patients (535 EGC lesions) underwent ESD. Of these, 452 were curative (84.7%), and 83 (15.5%) were non-curative. Multivariate analysis revealed that location in the body and fundus or cardia of the stomach, larger tumor size (≥ 30 mm), and histological undifferentiated type were independent risk factors for submucosal invasion and deep submucosal invasion in patients with EGC (all P < 0.05). Multivariate analysis showed that tumor size of 20 ~ 29 mm, tumor size ≥ 30 mm, elevated lesions, depressed lesions, undifferentiated tumors and submucosal invasion were all independent predictors of NCR for EGCs (all P < 0.05). The area under the ROC curve (AUC) of the nomogram model for predicting submucosal invasion and non-curative resection was 0.821 (95% CI, 0.758 ~ 0.884) and 0.937 (95%CI, 0.889 ~ 0.985), respectively.ConclusionsWe developed nomograms to predict the risk of submucosal invasion and NCR prior to ESD. These predictive factors in addition to the existing ESD criteria can help provide the best treatment option for patients with EGC.

Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and cognitive impairment in patients with acute mild ischemic stroke

BackgroundThe non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is a recently developed lipid parameter, but there are insufficient studies exploring its relationship with early cognitive impairment in patients with acute mild stroke. This study aims to determine the potential association between NHHR and early cognitive impairment in patients with acute mild stroke. By collecting data from patients with acute minor ischemic stroke in hospital, we will analyze the relationship between NHHR and cognitive function in these patients.MethodsThis study enrolled 817 acute ischemic stroke (AIS) patients (NIHSS ≤ 5), Cognitive function was assessed using Mini-Mental State Examination (MMSE) within 2 weeks, with cognitive impairment defined by education-stratified thresholds. Statistical analysis of the baseline was performed. Multivariate logistic regression was performed to analyze the association between NHHR and cognitive impairment, and Receiver Operating Characteristic Curve (ROC) analysis were performed to evaluate the predictive value.ResultsPatients were classified into cognitive impairment group (n = 473) and normal cognition group (n = 344). NHHR in the cognitive impairment group was significantly higher than that in the normal group (3.24 ± 1.63 vs. 3.02 ± 1.43, P = 0.046). There were significant differences in age and education level. There was a dose–response relationship between NHHR quartiles and the incidence of cognitive impairment (trend test P = 0.021). Multivariate regression analysis showed that for each unit increase in NHHR, the risk of cognitive impairment increases by 13.2% (OR = 1.13, 95% confidence interval 1.02–1.25, P = 0.018). The predictive model constructed by combining age and education level has an area under the ROC curve(AUC) of 0.71 (95% confidence interval 0.67–0.74).ConclusionsNHHR is an independent risk factor for early cognitive impairment in mild AIS patients. The NHHR-based model demonstrates moderate predictive accuracy, supporting its potential clinical utility.

Influence function-based empirical likelihood for area under the receiver operating characteristic curve in presence of covariates.

In receiver operating characteristicROC analysis, the area under the ROC curve (AUC) is a popular one number summary of the discriminatory accuracy of a diagnostic test. AUC measures the overall diagnostic accuracy of a test but fails to account for the effect of covariates when covariates are present and associated with the test results. Adjustment for covariate effects can greatly improve the diagnostic accuracy of a test. In this paper, using information provided by the influence function, empirical likelihood (EL) methods are proposed for inferences of AUC in presence of covariates. For parameters in the AUC regression model, it is shown that the asymptotic distribution of the influence function-based empirical log-likelihood ratio statistic is a standard chi-square distribution. Hence, confidence regions for the regression parameters can be obtained without any variance estimation. Simulation studies are conducted to compare the finite sample performances of the proposed EL based methods with the existing normal approximation (NA) based method in the AUC regression. Simulation results indicate that the bootstrap-calibrated influence function-based empirical likelihood (BIFEL ) confidence region outperforms the NA-based confidence region in terms of coverage probability. We also propose an interval estimation method for the covariate-adjusted AUC based on the BIFEL confidence region. Finally, we illustrate the recommended method with a real prostate-specific antigen data example.

A preliminary exploration of a predictive model and nomogram for the efficacy of compound digestive enzyme therapy based on serum (PGI, PGII, VIP, and PRDX1) in patients with functional dyspepsia

ObjectiveThis study aimed to explore the feasibility of constructing compound digestive enzyme therapeutic effect prediction model based on serum pepsinogen I (PGI), pepsinogen II (PGII), vasoactive intestinal peptide (VIP), and peroxidase 1 (PRDX1) in patients with functional dyspepsia (FD), and draw nomograms, to provide reference for the selection of clinical treatment.MethodsA total of 249 FD patients who visited the Department of Gastroenterology in our hospital from January 2021 to December 2024 were selected, and the preoperative clinical and laboratory indicators were collected. the patient cohort was split in a 7:3 ratio into a training set (n = 174) and a validation set (n = 75). The risk factors were screened by univariate and multivariate logistic regression in the training set, and the nomogram model was constructed. The receiver operating characteristic curve (ROC) was drawn and the calibration curve was used to evaluate the effectiveness of the model. The model was verified in the verification set, and the clinical value was evaluated by decision curve analysis (DCA).ResultsThe results of multivariate logistic regression showed that PGI, PGII, VIP, PRDX1, white blood cell count, aspartate aminotransferase and high density lipoprotein cholesterol were the independent risk factors for poor efficacy of compound digestive enzymes in the treatment of FD. The C-index was 0.830 and 0.827, respectively, the area under the ROC curve (AUC) was 0.835 (95% CI: 0.792–0.941) and 0.835 (95% CI: 0.687–0.983), and the sensitivity and specificity were 0.768, 0.857, and 0.778, 0.780, respectively.ConclusionThe therapeutic effect prediction model of compound digestive enzyme base on serum PGI, PGII, VIP, PRDX1 in patients with FD has some clinical value, but it still need to be further verified by large sample size and multi-center study.

Evaluation of sarcopenia diagnosis strategies in Chinese community-dwelling older adults based on the 2019 Asian Working Group guidelines: a cross-sectional study

BackgroundMost diagnostic studies on sarcopenia in Asia follow the 2019 Asian Working Group for Sarcopenia (AWGS) guidelines, which recommend distinct diagnostic strategies for community and hospital settings due to challenges in measuring muscle mass in community environments. This study evaluates the screening-to-diagnosis process in community-based preventive services.MethodsThis cross-sectional study utilized a questionnaire survey to evaluate SARC-F and SARC-CalF. Measurements included calf circumference (CC), handgrip strength, gait speed and bioelectrical impedance analysis (BIA). Participants were diagnosed according to the AWGS 2019 criteria. Four scenarios simulating the screening-to-diagnosis process in a community setting were evaluated. Sensitivity, specificity, and the area under the ROC curve (AUC) were calculated to assess diagnostic performance.ResultsA total of 2453 community-dwelling older adults aged ≥60 years were included. The prevalence of sarcopenia was 14.1% (345/2453), with rates of 15.4%(160/1038) in males and 13.1% (185/1415) in females. In the simulated diagnostic scenarios, the number of confirmed cases was 218 (combination,Scenario1), 211 (CC,Scenario2), 60 (SARC-CalF,Scenario3) and 21 (SARC-F,Scenario4), respectively. In the case-finding step, the sensitivity for Scenarios1 to 4 was 0.86,0.84,0.23 and 0.07, respectively; specificity was 0.57,0.58,0.93 and 0.99, respectively; and the AUCs were 0.717,0.710,0.581 and 0.530, respectively. In the assessment step, the sensitivity for Scenarios 1 to 4 was 0.73,0.73,0.74 and 0.88, respectively; specificity was 0.81,0.82,0.68 and 0.24, respectively; and the AUCs were 0.774,0.774,0.712 and 0.557,respectively. The integrated sensitivity of the case-finding and assessment steps for Scenarios 1 to 4 was 0.63,0.61,0.17 and 0.06, respectively; integrated specificity was 0.92,0.92,0.98 and 0.99, respectively; and integrated AUCs were 0.776,0.768,0.575 and 0.523, respectively. The diagnostic performance of the entire procedure was better in females than in males.ConclusionsIn the case-finding step, the CC tool demonstrated superior performance compared to the combination tool, SARC-CalF, and SARC-F. In the assessment step, the muscle strength test was consistently performed with stability. The integrated performances of the case-finding and assessment steps exhibited moderate accuracy in Scenarios 1 and 2, but low accuracy in Scenarios 3 and 4. There is a pressing need to develop more accurate and user-friendly tools to improve sarcopenia detection among community-dwelling older adults in China.

Projecting the effect of climate change on multiple Geomorphological hazard using machine learning data driven approaches

Land subsidence (LS) and collapsed pipes (CP) pose environmental and socio-economic threats in arid and semi-arid regions. This study assesses the effect of climate change to address these problems in Khorasan-Razavi province, Iran. Thus, we mapped soil landforms susceptible to LS and CP based on climatic, geolocic, topoghraphic, hydrologic and edaphic variables using an ensemble forecasting approach. Additionally, we predicted the future susceptibility of CP and LS based on two future emission scenario pathways (SSP 5-8.5 and SSP 1-2.6), in 2030, 2050, 2070, and 2090. The assessment showed that the area under the ROC curve (AUC) indicated that the ensemble model accurately predicted the distribution of CP and LS (AUC > 0.8). Slope and clay content proved to be the most important factors affecting CP, whereas distance from faults and precipitation seasonality played more roles in LS susceptibility. The classification results indicated varying susceptibility levels to CP and LS in Khorasan-Razavi province, with approximately 31.58% categorized as low and 15.24% as very high LS susceptibility, while 42.71% were in the low CP susceptibility class. Overall, 57.16% of the area is safe from both hazards; however, 6.16% is vulnerable to both hazards, with more than 35% at risk for at least one hazard. Future prediction models suggest that up to approximately 4% of the area will consist susceptible to both hazards under both scenario emissions and less than 1% of the study area will reduce susceptibility for both studied hazards in future. The majority of regions that remain susceptible are in the southern province. These results guide for soil management to protect soil and water from the effects of humans and climate alternation in poor areas worldwide.

Conventional (D-dimer) and potential (pentraxin 3 and sST2) biomarkers in long-term prognosis of adverse cardiovascular events in COVID-19 survivors without significant cardiovascular diseases

Aim. To determine the potential role of conventional and potential biomarkers in predicting major adverse cardiovascular events (MACE) in the long-term period after coronavirus disease 2019 (COVID-19).Material and methods. On the day of hospitalization, 112 inpatients with a confirmed diagnosis of COVID-19 were assessed for biomarkers such as high-sensitivity troponin T (hsTnT) and troponin I (hsTnI), N-terminal pro-brain natriuretic peptide (NT-proBNP), D-dimer, so­luble tumorigenicity suppression protein (sST2) and pentraxin 3 (PTX3). COVID-19 survivors were followed for a median period of 366 [365; 380] days after discharge from the COVID hospital, assessing the incidence of MACE (myocardial infarction, pulmonary embolism, cereb­rovascular accident, cardiovascular death).Results. During the one-year follow-up period, the study endpoints (MACE) were registered in 14 (12,5%) patients. Of the cardiovascular biomarkers studied, differences were found in the levels of both conventional (hsTnT, D-dimer) and potential biomarkers (sST2, PT3) in the groups of patients with and without MACE. Groups did not differ significantly in NT-proBNP and hsTnI levels (p&gt;0,05). According to multivariate analysis, the strongest predictors of MACE development are body mass index &gt;29,5 kg/m2 (Area Under The ROC Curve (AUC) 0,672, sensitivity 45%, specificity 23,9%, p=0,001), PTX3 &gt;3,1 ng/ml (AUC 0,885, sensitivity 94,0%, specificity 82,1%, p=0,001), sST2 &gt;36 ng/ml (sensitivity 92,9%, specificity 33%, p=0,001), D-dimer &gt;0,4 μg/ml (AUC 0,787, sensitivity 93%, specificity 72,4%, p=0,049). A mathematical model based on the concentration of PTX3, sST2 and D-dimer biomarkers predicts MACE within 1 year after COVID-19 with a sensitivity of 92,9%, specificity of 61% and predictive accuracy of 90,5% (p&lt;0,001).Conclusion. Determination of the concentration of biomarkers such as D-dimer, sST2, PT3 can be used to predict long-term MACE in patients after COVID-19.

Detection of Early-Stage Colorectal Cancer Using Cell-Free oncRNA Biomarkers and Artificial Intelligence.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide, and early detection significantly improves treatment outcomes, but existing blood-based tests often have limited sensitivity in early-stage disease. We developed a blood-based test combining oncRNAs, a group of small cell-free RNAs (cfRNAs), with generative AI to detect CRC. We leveraged a cohort of 613 CRC cases and controls to train a model that demonstrated both high clinical performance as well as minimal technical variability in robustness testing. We further validated our model in an independent, single-source cohort of 192 CRC cases and controls. Model performance was assessed by sensitivity, specificity, and area under the ROC curve (AUROC), with attention to early-stage detection. In our independent validation set, we achieved an overall sensitivity of 89% at 90% specificity, with an 80% sensitivity for stage I-an important milestone, as early-stage CRC detection remains a challenge for other blood-based technologies. Performance was consistent across demographic subgroups. Our oncRNA-based blood test, powered by AI, offers strong performance for early CRC detection, including in stage I disease where existing blood-based assays are limited. These findings support further development toward a minimally invasive CRC screening tool.

FLAIR-based radiomics signature from brain-tumor interface for early prediction of response to EGFR-TKI therapy in NSCLC patients with brain metastasis.

Evaluating response to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) is crucial in non-small cell lung cancer (NSCLC) patients with brain metastases (BM). To explore values of multi-sequence MRI in early assessing response to EGFR-TKIs in non-small cell lung cancer (NSCLC) patients with BM. A primary cohort of 133 patients (January 2018 to March 2024) from center one and an external cohort of 52 patients (May 2017 to December 2022) from center two were established. Radiomics features were extracted from 4 mm brain-tumor interface (BTI) and whole BM region across T1-weighted contrast enhanced (T1CE) and T2-weighted (T2W) and T2 fluid-attenuated inversion recovery (T2-FLAIR) MRI sequences. The most relevant features were selected using the U test and least absolute shrinkage and selection operator (LASSO) method to develop the multi-sequence models based on BTI (RS-BTI-COM) and BM (RS-BM-COM). By integrating RS-BTI-COM with peritumoral edema volume (VPE), the combined model was built using logistic regression. Model performance was evaluated using the area under the ROC curve (AUC), sensitivity (SEN), specificity (SPE) and accuracy (ACC). The constructed RS-BTI-COM demonstrated a higher association with early response to EGFR-TKI therapy than RS-BM-COM. The combined RS-BTIplusVPE, incorporating BTI-based radiomics features and VPE, exhibited the highest AUCs (0.843-0.938), SPE (0.808-0.905) and ACC (0.712-0.875) in the training, internal validation, and external validation cohort, respectively. The study developed a validated non-invasive model (RS-BTIplusVPE) based on integrating BTI-based radiomics features and VPE, which showed improved prediction of EGFR-TKI response in NSCLC patients with BM compared to tumor-focused models.

Early-Stage Diabetic Retinopathy Diagnosis with Feature Pyramid Networks and Spatial Pyramid Pooling Utilizing Full-Field Optical Coherence Tomography (FF-OCT)

Diabetes is a disease that is growing at the highest rate globally, and it has several impacts. One of such complications is diabetic retinopathy (DR), which causes damage to the retina and vision impairment. DR can be mild, and it can also be severe, and there are many shades in between. Therefore, it is possible to avoid vision loss if DR is diagnosed and treated in its early stages. Diagnosis of DR today is still a tedious process as only ophthalmologists can diagnose it using digitized colorful retinal fundus images. This article describes a new strategy for the early identification of DR that combines full-field optical coherence tomography (FF-OCT) with sophisticated deep learning (DL) algorithms, notably Feature Pyramid Networks (FPNs) linked with spatial pyramid pooling (SPP). FF-OCT produces high-resolution, cross-sectional pictures of the retina, revealing precise structural changes that occur before apparent symptoms of DR. We created a multiscale DL framework that takes advantage of the capabilities of FPNs and SPP to efficiently process and evaluate the fine features of FF-OCT images. The FPN design detects changes in the retinal structure at different scales, which improves the network's performance in detecting early signs of DR. On the other hand, the SPP module collects contextual information from several sub-regions of the image to provide a stable and accurate feature representation regardless of the size and location of the lesion. These models were trained and validated on this dataset using performance indicators such as sensitivity, specificity, and area under the ROC curve (AUC). The findings of this paper suggest that the FPNs with the SPP model are superior to the traditional image analysis methods and the standard Convolutional Neural Network in diagnosing early-stage DR. Received: 9 November 2024 | Revised: 10 March 2025 | Accepted: 3 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/paultimothymooney/kermany2018. Author Contribution Statement Anitha Jaikumar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Sreenivasa Chakravarthi Sangapu: Conceptualization, Formal analysis, Investigation, Resources, Writing – review &amp; editing, Supervision, Project administration.

Automated Detection of Anomalies in Healthcare Data Using Machine Learning

The health care industry creates vast amounts of data each day, from electronic health records (EHRs) and lab test results to radiological images and outputs from wearable devices. This expanding reservoir of information offers an unprecedented potential for improving patient outcomes through data-driven decision making. Yet, the complexity and high dimensionality of healthcare data also pose significant risks, particularly in terms of errors, fraud, and missed clinical events. Manual anomaly reviewing and detection mechanisms tend to be inefficient, error-prone, and non-scalable. To circumvent these downsides, autonomous anomaly detection methods based on ML algorithms have increasingly become popular.This paper identifies how different algorithms of ML may be used in order to find anomalies in medical data efficiently. We examine both supervised and unsupervised algorithms like Support Vector Machines (SVM), Random Forests, Isolation Forests, Autoencoders, and k-Nearest Neighbors (k-NN). These models are compared in terms of their precision, recall, F1-score, and area under the ROC curve (AUC) on real-world datasets from hospital databases and publicly available healthcare repositories. The methodology section explains data preprocessing, model training, hyperparameter tuning, and validation techniques. Our experimental results show that ensemble models and deep learning architectures tend to outperform conventional methods in both accuracy and robustness, particularly in dealing with imbalanced datasets. In addition, we discuss the operational challenges in implementing these systems, such as data privacy issues, interpretability of sophisticated models, integration with hospital information systems in place, and regulatory compliance. The discussion presents solutions like differential privacy, explainability frameworks for models, and continuous learning systems to address these challenges. In conclusion, the results highlight the revolutionary potential of machine learning to improve anomaly detection, thus ensuring patient safety, optimizing healthcare provision, and enabling real-time clinical decision-making.