Perfect Recall Research Articles

Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer.

To evaluate the performance of vision transformer-derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging. Computational study using endoscopic frames with a focus on the application of a self-supervised vision transformer model (DINOv2) for tissue classification. High-definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings. These embeddings served as input for a standard support vector machine (SVM) to classify the tissues as neoplastic or normal. The model's discriminative performance was validated using an 80-20 train-validation split. From 38 endoscopic NBI videos, 327 image patches were analyzed. The classification results in the validation cohort demonstrated high accuracy (92%) and precision (89%), with a perfect recall (100%) and an F1-score of 94%. The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.96. The use of large vision model-derived embeddings effectively differentiated between neoplastic and normal oropharyngeal tissues. This study supports the feasibility of employing CV foundation models like DINOv2 in the endoscopic evaluation of mucosal lesions, potentially augmenting diagnostic precision in Otorhinolaryngology. 4 Laryngoscope, 134:4535-4541, 2024.

Different strokes in randomised strategies: Revisiting Kuhn's theorem under finite-memory assumptions

Two-player (antagonistic) games on (possibly stochastic) graphs are a prevalent model in theoretical computer science, notably as a framework for reactive synthesis.Optimal strategies may require randomisation when dealing with inherently probabilistic goals, balancing multiple objectives, or in contexts of partial information. There is no unique way to define randomised strategies. For instance, one can use so-called mixed strategies or behavioural ones. In the most general setting, these two classes do not share the same expressiveness. A seminal result in game theory — Kuhn's theorem — asserts their equivalence in games of perfect recall.This result crucially relies on the possibility for strategies to use infinite memory, i.e., unlimited knowledge of all past observations. However, computer systems are finite in practice. Hence it is pertinent to restrict our attention to finite-memory strategies, defined as automata with outputs. Randomisation can be implemented in these in different ways: the initialisation, outputs or transitions can be randomised or deterministic respectively. Depending on which aspects are randomised, the expressiveness of the corresponding class of finite-memory strategies differs.In this work, we study two-player concurrent stochastic games and provide a complete taxonomy of the classes of finite-memory strategies obtained by varying which of the three aforementioned components are randomised. Our taxonomy holds in games of perfect and imperfect information with perfect recall, and in games with more than two players. We also provide an adapted taxonomy for games with imperfect recall.

Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM).

Microplastic pollution poses a significant threat to our environment, necessitating effective predictive modelling approaches for better management and mitigation. In this study, we introduce a pioneering methodology that fuses the power of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) for microplastic predictive modelling. Leveraging a comprehensive dataset, our integrated model exhibits exceptional performance, with an Accuracy of 0.96, Precision of 0.96, Recall of 0.97, and an F1 Score of 0.96. The achieved Accuracy underscores the model's proficiency in distinguishing microplastic and non-microplastic entities, promising robust and reliable predictions. Precision, as a measure of correct positive identifications, demonstrates our model's effectiveness in minimizing false positives, a crucial aspect for environmental monitoring. Moreover, the perfect Recall score signifies the model's ability to detect all relevant microplastic instances, addressing concerns about false negatives. The F1 Score encapsulates this dual proficiency, showcasing a harmonious trade-off between precision and recall. Our research not only advances the field of microplastic prediction but also highlights the potential of synergizing ANN and HMM methodologies for comprehensive environmental assessments. The reported performance metrics underscore the practical applicability of our approach, offering a valuable tool for tackling the pervasive issue of microplastic pollution and fostering proactive environmental stewardship.

A Novel Deep Learning Approach for Detecting Types of Attacks in the NSL-KDD Dataset

The growing prevalence of Internet intrusions poses significant threats to the security, privacy, and reliability of systems and networks. Denial-of-service (DoS) attacks are a cause for concern as they aim to disrupt access to network resources, posing major risks. Traditional intrusion detection systems (IDS) face challenges in detecting attacks because of the evolving nature of these attacks. Therefore, advanced techniques are necessary to accomplish accurate and timely detection. This study introduces a novel approach that combines Deep learning techniques, specifically the CNN algorithm, with Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) for the purpose of feature selection. The effectiveness and efficiency of our method are shown by rigorous testing on DDoS datasets. We present a novel Fast Hyper Deep Learning Model that attains a remarkable accuracy of 99%, along with perfect recall and F1-measurement scores of 100%. This model surpasses existing methodologies by a significant margin. The NSL-KDD data set allows for achieving a level of precision of100%.

Enhancing Firewall Packet Classification through Artificial Neural Networks and Synthetic Minority Over-Sampling Technique: An Innovative Approach with Evaluative Comparison

Firewall packet classification is a critical component of network security, demanding precise and reliable methods to ensure optimal functionality. This study introduces an advanced approach that combines Artificial Neural Networks (ANNs) with various data balancing techniques, including the Synthetic Minority Over-sampling Technique (SMOTE), ADASYN, and BorderlineSMOTE, to enhance the classification of firewall packets into four distinct classes: ‘allow’, ‘deny’, ‘drop’, and ‘reset-both’. Initial experiments without data balancing revealed that while the ANN model achieved perfect precision, recall, and F1-Scores for the ‘allow’, ‘deny’, and ‘drop’ classes, it struggled to accurately classify the ‘reset-both’ class. To address this, we applied SMOTE, ADASYN, and BorderlineSMOTE to mitigate class imbalance, which led to significant improvements in overall classification performance. Among the techniques, the ANN combined with BorderlineSMOTE demonstrated superior efficacy, achieving a 97% overall accuracy and consistently high performance across all classes, particularly in the accurate classification of minority classes. In contrast, while SMOTE and ADASYN also improved the model’s performance, the results with BorderlineSMOTE were notably more balanced and reliable. This study provides a comparative analysis with existing machine learning models, highlighting the effectiveness of the proposed approach in firewall packet classification. The synthesized results validate the potential of integrating ANNs with advanced data balancing techniques to enhance the robustness and reliability of network security systems. The findings underscore the importance of addressing class imbalance in machine learning models, particularly in security-critical applications, and offer valuable insights for the design and improvement of future network security infrastructures.

Artificial intelligence-empowered assessment of bile duct stone removal challenges

The aim of this investigation was to unravel the complexities inherent in endoscopic retrograde cholangiopancreatography (ERCP) procedures for bile duct stone removal by leveraging advanced artificial intelligence (AI) methodologies to support clinical decision-making and optimize patient outcomes. We introduced the Assessment of Stone Removal Challenges (ACRS) system, a novel integration of Data-efficient Image Transformers (DeiT) for image data analysis and eXtreme Gradient Boosting (XGBoost) for clinical data interpretation. Our study included a patient cohort of 2,129 individuals, focusing on training the ACRS system to achieve high diagnostic precision. Using logistic regression, we identified pivotal predictors affecting the complexity of bile duct stone removal. These findings were visually represented through forest plots and nomograms. Analytical and visualization processes were conducted using the Python and R programming languages, adhering to a p value significance threshold of less than 0.05. By utilizing DeiT enhanced by transfer learning and XGBoost for clinical data interpretation, the system achieved an accuracy of 0.83 and a perfect recall rate on a test set of 2,129 patients. Gradient-weighted class activation mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) provided in-depth diagnostic insights. Logistic regression was applied to identify crucial clinical predictors for stone removal difficulty, as visualized through forest plots and nomograms. These tools facilitated measurable assessments of procedural complexity, making significant strides in gastroenterology diagnostics and decision-making. By adopting causal inference techniques, the system effectively quantifies the influence of various clinical entities on stone removal difficulty, thereby augmenting both diagnostic precision and procedural strategies in ERCP.

A Comprehensive Approach for an Interpretable Diabetic Macular Edema Grading System Based on ConvUNext

Diabetic macular edema (DME) is a leading cause of vision impairment in diabetic patients, necessitating a timely and accurate diagnosis. This paper proposes a comprehensive system for DME grading using retinal fundus images. Our approach integrates multiple deep learning modules, each designed to address key aspects of the diagnostic process. The first module employs the ConvUNeXt model for segmenting hard exudates (HaEx), crucial indicators of DME. The second module uses RetinaNet for precise optic disc (OD) localization, which is essential for subsequent macula localization. The third module refines macula localization, leveraging preprocessing techniques to enhance image clarity. Finally, our system consolidates these results to provide interpretable DME grading. Experimental evaluations were conducted on the Messidor dataset, demonstrating the system’s robust performance. The HaEx segmentation module achieved a mean IoU of 70.5% and a Dice coefficient of 0.64. The OD localization module showed perfect accuracy, recall, and precision at 1.0. For macula localization, our method satisfied the 1R criterion with 99.38% accuracy. The DME grading module achieved an overall accuracy of 91.12%, with an AUC of 0.9334. Our method offers a balanced performance across accuracy, sensitivity, and specificity compared to other non-interpretable and partially interpretable methods.

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.

Machine Learning Approach for Early Diagnosis of Dyslexia Among Primary School Children: A Scoping Review and Model Development

Dyslexia, a prevalent learning disorder among primary school children, often goes undetected until later stages, hindering academic progress and socio-emotional development. Early diagnosis is crucial for effective intervention. Machine Learning (ML) offers promise in developing accurate diagnostic tools. However, there's a scarcity of comprehensive reviews focusing on ML approaches for dyslexia diagnosis in this demographic. In this scoping review, we consolidate existing literature and present the development of a novel ML model that was customized for early dyslexia diagnosis. Utilizing Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest. The comparative analysis of ML methods for dyslexia detection in elementary school children reveals distinct strengths. Decision Tree shows robust precision: 92.31% for dyslexia-prone, 90.62% for diagnosed dyslexia, and 86.67% for no dyslexia detected, with corresponding high recall values of 90.57%, 87.88%, and 100%, respectively. KNN excels with an overall accuracy of 94.00% and perfect precision for undetected dyslexia (100%), with high precision and recall for dyslexia-prone and diagnosed dyslexia. Logistic Regression highlights significant predictors and achieves precision of 95.38% for dyslexia-prone and 88.24% for diagnosed dyslexia, with recall rates of 93.34% and 90.91%, respectively. Naive Bayes exhibits outstanding precision for no dyslexia and dyslexia-prone categories (100%), with slightly lower precision for diagnosed dyslexia (82.5%), but perfect recall for undetected and diagnosed dyslexia. Random Forest demonstrates balanced performance with precision ranging from 91.18% to 94.23% and recall from 92.31% to 93.94%, achieving an overall accuracy of 93.00%. These results underscore ML's potential in enabling early dyslexia detection, facilitating timely interventions to improve outcomes for affected children and advancing dyslexia diagnosis.

ToPick: Time-of-Pickup Measurement for the Elderly using Wearables.

The ability to pick up objects off the floor can degrade over time with elderly individuals, leading to a reduced quality of life and an increase in the risk of falling. Healthcare professionals have expressed an interest in monitoring the decline in pickup ability of a subject over extended periods of time and intervening when it becomes hazardous to the subject's health. The current means of evaluating pickup ability involving in-clinic patient visits is both time and financially expensive. There is a clear need for a cost-effective, remote means of pickup evaluation to ease the burden on both patients and physicians. To address these challenges, we introduce a Time-of-Pickup (ToP) solution, called ToPick, designed for the automatic assessment of pickup ability over time. The practical performance of ToPick is evident, demonstrated by a minimal median error of approximately 100 milliseconds in evaluating 20 pickup events among 10 elderly individuals. Furthermore, ToPick exhibits a high level of reliability, achieving perfect accuracy, precision, and recall scores for pickup event detection. We actualize our research findings by designing an application intended for adoption by both healthcare practitioners and elderly individuals. The app aims to reduce both time and financial costs while enabling mobile treatment for users.

Comparative analysis of computer vision algorithms for the real-time detection of digital dermatitis in dairy cows

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the planar aspect of the hoof. DD is associated with massive herd outbreaks of lameness and influences cattle welfare and production. Early detection of DD can lead to prompt treatment and decrease lameness. Computer vision (CV) provides a unique opportunity to improve early detection. The study aims to train and compare applications for the real-time detection of DD in dairy cows. Eight CV models were trained for detection and scoring, compared using performance metrics and inference time, and the best model was automated for real-time detection using images and video. Images were collected from commercial dairy farms while facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system with distinct labels for hyperkeratosis (H) and proliferations (P). Two sets of images were compiled: the first dataset (Dataset 1) containing 1,177 M0/M4H and 1,050 M2/M2P images and the second dataset (Dataset 2) containing 240 M0, 17 M2, 51 M2P, 114 M4H, and 108 M4P images. Models were trained to detect and score DD lesions and compared for precision, recall, and mean average precision (mAP) in addition to inference time in frame per second (FPS). Seven of the nine CV models performed well compared to the ground truth of labeled images using Dataset 1. The six models, Faster R-CNN, Cascade R-CNN, YOLOv3, Tiny YOLOv3, YOLOv4, Tiny YOLOv4, and YOLOv5s achieved an mAP between 0.964 and 0.998, whereas the other two models, SSD and SSD Lite, yielded an mAP of 0.371 and 0.387 respectively. Overall, YOLOv4, Tiny YOLOv4, and YOLOv5s outperformed all other models with almost perfect precision, perfect recall, and a higher mAP. Tiny YOLOv4 outperformed all other models with respect to inference time at 333 FPS, followed by YOLOv5s at 133 FPS and YOLOv4 at 65 FPS. YOLOv4 and Tiny YOLOv4 performed better than YOLOv5s compared to the ground truth using Dataset 2. YOLOv4 and Tiny YOLOv4 yielded a similar mAP of 0.896 and 0.895, respectively. However, Tiny YOLOv4 achieved both higher precision and recall compared to YOLOv4. Finally, Tiny YOLOv4 was able to detect DD lesions on a commercial dairy farm with high performance and speed. The proposed CV tool can be used for early detection and prompt treatment of DD in dairy cows. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time DD detection on dairy farms.

Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.

Measuring interpersonal firearm violence: natural language processing methods to address limitations in criminal charge data.

Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related. We accessed and digitized court records from the state of Washington (n = 1472). Human review established a gold standard label for firearm involvement (yes/no). We developed a key term search and trained supervised machine learning classifiers for this labeling task. Results were evaluated in a held-out test set. The decision tree performed best (F1 score: 0.82). The key term list had perfect recall (1.0) and a modest F1 score (0.65). This case report highlights the accuracy, feasibility, and potential time-saved by using NLP to identify firearm involvement in alleged violent crimes based on digitized narratives from court documents.

Information flows and memory in games

The standard extensive-form partitional representation of information in sequential games fails to distinguish the description of the rules of interaction from the description of players' personal traits. Indeed, this representation does not model how the information given to players as per the rules of the game blends with players' cognitive abilities. We propose a representation of sequential games that explicitly describes the flow of information accruing to players rather than the stock of information retained by players encoded in information partitions. Then, we add a game-independent description of players' mnemonic abilities. If players have perfect memory, our flow representation gives rise to information partitions satisfying the perfect recall property, but different combinations of information flows and players' mnemonic abilities may induce the same information partitions. We show how to use our framework to explicitly model a wide array of cognitive limitations and embed them in the representation of game situations.

Crop Recommendation Predictive Analysis using Ensembling Techniques

Crop recommendation systems play a crucial role in modern agriculture by aiding farmers in making well-informed choices to optimize crop yield and resource utilization. Ensemble learning approaches can significantly improve the effectiveness of crop recommendation systems. To achieve this, multiple forecasts are combined from various models. In this paper, a complete Machine Learning Pipeline is used to evaluate the performance of ensemble learning models in crop recommendation tasks. A diverse dataset is used to select and train four ensemble learning methods, Bagging, Voting, Stacking, and One-Vs-Rest (OVR), as separate classifiers. The dataset includes various agricultural factors such as soil characteristics, meteorological conditions, and past crop productivity. Various metrics, including accuracy, precision, recall, F1-score, and support, are utilized for each model. Bagging is considered the most effective ensemble learning technique, demonstrating excellent levels of accuracy and overall performance. The bagging algorithm achieves a high level of accuracy, reaching 99.32%. It also achieves perfect precision, recall, and F1-score metrics, with values of 0.99, 1.00, and 1.00 respectively. The support value, which represents the number of instances used for evaluation, is 141. This study provides valuable perspectives on the choice of appropriate ensemble learning models for crop recommendation tasks. Consequently, it enables farmers and other individuals involved in agriculture to make well-informed choices using data, resulting in enhanced agricultural output and sustainability.

On the Complexity of Model Checking Knowledge and Time

We establish the precise complexity of the model-checking problem for the main logics of knowledge and time. While this problem was known to be non-elementary for agents with perfect recall, with a number of exponentials that increases with the alternation of knowledge operators, the precise complexity of the problem when the maximum alternation is fixed has been an open problem for 20 years. We close it by establishing improved upper bounds for CTL * with knowledge and providing matching lower bounds that also apply for epistemic extensions of LTL and CTL . We also study the model-checking problem for these logics on systems satisfying the “no learning” property, introduced by Halpern and Vardi in their taxonomy of logics of knowledge and time, and we settle the complexity in almost all cases.

Utilizing decision tree machine learning model to map dental students’ preferred learning styles with suitable instructional strategies

BackgroundGrowing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students’ preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students.MethodsA total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.ResultsThe application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.ConclusionThe decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students’ learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.

DQ-DeepLearn: Data Quality Driven Deep Learning Approach for Enhanced Predictive Maintenance in Smart Manufacturing

In the realm of smart manufacturing, predictive maintenance plays a pivotal role in ensuring equipment reliability, minimizing downtime, optimizing costs, and reducing product failure rate by detecting faulty products in the early stage. However, the efficacy of predictive maintenance hinges on the quality of training data employed for predictive modeling. Inadequate data quality can lead to erroneous predictions and, consequently, ineffective maintenance strategies. This research paper introduces a solution that leverages deep learning and data quality management to enhance predictive maintenance in smart manufacturing. Our proposed methodology encompasses two major components such as data quality management and faulty product detection. Data quality management includes data preprocessing, feature reduction, and data balancing, whereas faulty product detection is done using deep learning techniques. By combining these elements, a predictive model capable of accurately forecasting faulty products in early stages to reduce economic losses is developed. The proposed approach effectively addresses data quality issues and is tested on the SECOM dataset which indicates that it surpasses traditional models with an accuracy of 96.4% and perfect recall. Ultimately, this research contributes to the advancement of predictive maintenance and deep learning in the context of smart manufacturing, benefiting both industrial practitioners and researchers alike.

Hybrid grey wolf and whale optimization for enhanced Parkinson's prediction based on machine learning models using biomedical sound

BackgroundBiomedical voice measurements have been used by many physicians and scientists to distinguish Parkinson's patients from ordinary people. Measurements of biomedical voices involve many variables calculated from signal analysis of the voice. These variables can be used to distinguish Parkinson's patients from non-Parkinson's patients. Unfortunately, using all computed variables may be ineffective and inaccurate due to the complexity of establishing a relationship between all the input variables and Parkinson's states. MethodsThis paper describes the development of a hybrid optimizer by combining two optimization algorithms: the Grey Wolf Optimizer and the Whale Optimizer. The hybrid optimizer enhances feature selection to provide a fast and robust Parkinson's prediction model. Additionally, this research incorporates five other feature selection algorithms for comparison purposes: Ranker, Greedy, BestFirst, Hybrid Grey Wolf Optimization, and Whale Optimization. The outputs from these algorithms are fed into six prediction models to determine the most accurate combination. These models include the neural network, Quest, Chi-squared Automatic Interaction Detection, support vector machine, CR-tree, and logistic regression models. Subsequently, the developed models are compared to identify the most accurate model based on various performance metrics. ResultsThe combination of the hybrid grey wolf and whale models yielded the highest scores in most metrics, achieving a perfect recall and a high F1 score. All models generated similar output, with an accuracy greater than 0.89. Quest, CR-tree, and neural networks are the most reliable and accurate models. ConclusionsBiomedical sound measurements can be used to develop an accurate and cost-effective Parkinson's prediction model.

Predicting Functional Outcome After Ischemic Stroke Using Logistic Regression and Machine Learning Models

This research employed binary logistic regression and machine learning techniques; Decision Tree, Random Forest, and Support Vector Machine (SVM), to predict functional outcomes following ischemic stroke. The main goal was to determine the most suitable model for the dataset through a comprehensive performance evaluation. Four models were examined for predicting post-ischemic stroke functional outcomes: Decision Tree, Random Forest, Logistic Regression, and SVM. The evaluation involved metrics such as Accuracy, Precision, F1-Score, and Recall. The Logistic Regression model achieved the highest accuracy at 90%, accurately predicting outcomes in 90% of cases. However, it had lower precision (50%), indicating an increased rate of false positive predictions. On the other hand, the SVM model displayed the highest precision (71.3%), implying fewer false positive predictions. It also attained the highest F1-Score (77.5%), indicating a strong balance between precision and Recall compared to the other models. Notably, the Logistic Regression model achieved perfect Recall (100%), correctly identifying all positive outcomes, while the Random Forest model showed significant recall performance (93.2%). Conversely, the Decision Tree model exhibited moderate accuracy (66.11%) but lower precision (66%), F1-Score (6.15%), and recall (3.2%), suggesting challenges with false positives and false negatives. Choosing the best model depends on analysis priorities. For accurate identification of positive outcomes, the Logistic Regression model's perfect recall is advantageous. For balanced performance, the SVM model's high F1-Score makes it a compelling option.