Machine Learning-based Prediction Model Research Articles

Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data.

Gallstone disease (GD) is a common gastrointestinal disease. Although traditional diagnostic techniques, such as ultrasonography, CT, and MRI, detect gallstones, they have some limitations, including high cost and potential inaccuracies in certain populations. This study proposes a machine learning-based prediction model for gallstone disease using bioimpedance and laboratory data. A dataset of 319 samples, comprising161 gallstone patients and 158 healthy controls, was curated. The dataset comprised 38 attributes of the participants, including age, weight, height, blood test results, and bioimpedance data, and it contributed to the literature on gallstones as a new dataset. State-of-the-art machine learning techniques were performed on the dataset to detect gallstones. The experimental results showed that vitamin D, C-reactive protein (CRP) level, total body water, and lean mass are crucial features, and the gradient boosting technique achieved the highest accuracy (85.42%) in predicting gallstones. The proposed technique offers a viable alternative to conventional imaging techniques for early prediction of gallstone disease.

A machine learning model to predict heart failure readmission: toward optimal feature set.

Hospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance. This study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model. Heart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC. A total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters. The proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.

INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE.

The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their intensive care unit (ICU) stay. Retrospective data was extracted from adult patients in the MIMIC-IV database who spent a minimum of 48 h in the ICU. Feature selection was performed using LASSO regression, and the dataset was balanced using the BL-SMOTE approach. Predictive models were built using six machine learning algorithms. The Shapley additive explanation algorithm was used to assess the impact of various clinical features in the optimal model, enhancing interpretability. The study included 26,346 ICU patients, of whom 379 (1.44%) were diagnosed with invasive fungal infection. The predictive model was developed using 20 risk factors, and the dataset was balanced using the borderline-SMOTE (BL-SMOTE) algorithm. The BL-SMOTE random forest model demonstrated the highest predictive performance (area under curve = 0.88, 95% CI = 0.84-0.91). Shapley additive explanation analysis revealed that the three most influential clinical features in the BL-SMOTE random forest model were dialysis treatment, APSIII scores, and liver disease. The machine learning model provides a reliable tool for predicting the occurrence of IFI in ICU patients. The BL-SMOTE random forest model, based on 20 risk factors, exhibited superior predictive performance and can assist clinicians in early assessment of IFI occurrence in ICU patients. Importance: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.

A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.

The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016-2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.

Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer-The BRAIN Study.

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2- breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2- breast cancer.

Machine learning-based prediction model for battery levels in IoT devices using meteorological variables

Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics.

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization.

To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods. In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient<9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI). The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811-0.851) in the training set, and 0.760 (95%CI: 0.722-0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764-0.814) in the training set and 0.731 (95%CI: 0.686-0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P<0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization. The Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.

Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model

BackgroundIdentification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid...

How to create graphs in hardware-constrained environments? Choosing the best creation approach via machine learning-based predictive models

How to create graphs in hardware-constrained environments? Choosing the best creation approach via machine learning-based predictive models

Vision-oriented machine learning-assisted seismic energy dissipation estimation for damaged RC beam-column connections

After a seismic event, damage quantification is necessary for safety evaluation, collapse proximity judgment, and measurement of the residual seismic capacity of the joints. Machine learning-based predictive models are developed in this paper aimed at the non-destructive and non-contact estimation of energy dissipation ratio for seismically damaged beam-column joints through surface crack texture complexity indices. For this purpose, the authors collected an experimental dataset comprising 934 images of cracked and/or crushed reinforced concrete-framed joints from 254 specimens under cyclic loading. The associated energy dissipation ratios are then extracted from the lateral load-displacement hysteresis curves for each image by the authors, along with the image-derived fractal indices. Machine learning models with six algorithms, including Linear Regression, Multi-Layer Perceptron, Decision Tree, Random Forest, Gradient Boost, and CatBoost, are trained by 80% of images as a training dataset and validated through the remaining 20% of images in two scenarios. The first scenario develops models by component geometric dimensions and generalized fractal indices calculated for the beams, columns, and joints of seismically damaged connections, while the second scenario adds structural parameters to visual indices to estimate the energy dissipation ratio as the target parameter. Performance evaluation of trained models in the mentioned scenarios via common correlation and accuracy measures, along with the k-fold cross-validation method, indicated that CatBoost-based ML models in both scenarios provide optimal results with R2 = 0.89, RMSE = 0.10 for scenario I and R2 = 0.92, RMSE = 0.09 for scenario II using testing dataset. The first scenario based on the CatBoost algorithm with the R2 variation range of 0.07 and RMSE variation range of 0.03 across the test folds provides a sufficient performance in terms of generalizability and accuracy for estimating the energy dissipation ratio through lower number of input variables.

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy. We utilized the "limma" package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identified using the "WGCNA" package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a prognostic feature associated with palmitoylation and developed a prognostic nomogram with the "regplot" package. The predictive values of the model for chemotherapy and immunotherapy responses were assessed using immunophenoscore (IPS) and the "pRophetic" package. We identified 211 differentially expressed genes related to palmitoylation, among which 44 demonstrated prognostic potential. Subsequently, a predictive model comprising eleven palmitoylation-related genes was developed. Patients were classified into high-risk and low-risk groups based on the median risk score. The findings revealed that individuals in the high-risk group exhibited lower survival rates, while those in the low-risk group showed increased immune cell infiltration and improved responses to chemotherapy and immunotherapy. Moreover, the BC-Palmitoylation Tool website was established. This study developed the first machine learning-based predictive model for palmitoylation-related genes and created a corresponding website, providing clinicians with a valuable tool to improve patient outcomes.

Integrating random forest model and internet of things-based sensor for smart poultry farm monitoring system

&lt;span&gt;The global poultry industry has encountered growing concerns related to foodborne illnesses, misuse of antibiotics, and environmental impacts. To tackle these issues, this study aims to develop an intelligent poultry farm with real-time environmental monitoring and predictive models. The primary objective is to combine a machine learning-based prediction model with internet of things (IoT) devices to gather and analyze environmental data, such as temperature, humidity, and ammonia levels, to forecast the conditions within poultry houses. These sensor data and additional information, such as feed consumption, water consumption, poultry weight, capacity, and poultry house dimensions will serve as inputs for supervised machine learning models. Among these models, the proposed random forest (RF) model, when augmented with timestamp features, achieves the highest accuracy rate of 96.665%, surpassing other models such as logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), support vector machine (SVM), and multi-layer perceptron (MLP) in identifying poultry house conditions. Additionally, this study demonstrates how the trained model can be effectively applied in a web-based monitoring system, delivering real-time data to farmers for well-informed decision-making and ultimately enhancing productivity in smart poultry farming.&lt;/span&gt;

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department

BackgroundMany prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.MethodsThe study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISKINDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISKINDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISKINDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results.DiscussionThis pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.Trial registrationClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830.

Enhancing Hospitalized Patients' Palliative Care Referrals via Machine Learning-Based Predictive Modeling within Electronic Health Record Systems

AbstractAccess to palliative care (PC) holds significance for hospital-admitted patients grappling with the symptoms of life limiting illnesses. Nonetheless, numerous such patients who could gain from PC fail to receive it promptly or even at all.We can leverage the prior year's historical data extracted from electronic health records of hospitalized patients to train a machine learning (ML) model. This model's purpose would be to prognosticate the requirement for PC consultation using real-time data. The model, operating as a semi-supervised system, will be integrated into institutional data pipelines, and utilized by a downstream display application overseen by the PC team. In cases where the PC team deems it suitable, a team member will communicate with the respective care team of the patient. The ML model's training efficacy will be assessed using the area under the curve (AUC) metric, employing a 20% reserved validation set. The threshold for PC consultations will be grounded in historical data. To enhance the ML model's precision, the pivotal variables within the model will be pinpointed, and any sources of biases or errors in the model will be identified for meticulous refinement. The AUC values of successive ML models will be juxtaposed with cross-validation data.Automatizing the referral procedure through electronic health record systems has the potential to usher in a more effective and streamlined approach to healthcare delivery.

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

BackgroundPrevious models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.MethodsThe electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively.ResultThe Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated.ConclusionsWe established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

Understanding macroscopic thermal conduction in composites reinforced with 2D nanosheets

Two-dimensional (2D) nanosheets, such as graphene and hexagonal boron nitride, are considered as the most promising fillers for enhancing thermal conductivity of polymers and phase-change materials. Nevertheless, the effect of various 2D nanosheets on the effective thermal conductivity of composites is not fully understood, and the corresponding prediction model is still lacking, since numerous influence factors and complex thermal transfer networks are involved. This paper aims to study the macroscopically effective thermal conductivity of the nanosheets-reinforced composites in a systematical way, and develop a robust machine learning based prediction model. To this end, a series of representative volume elements are reconstructed based on the SEM observations of experimental samples, and high-throughput simulations are performed via the updated lattice Boltzmann scheme proposed in our recent work. The effects of shape, size, orientation, intrinsic thermal conductivity, interface thermal resistance, surface coating, and hybrid filling of the 2D nanosheets are clarified. This work could provide a deep insight into the effective thermal conductivity of the nanosheets-reinforced composites, and may offer important guidelines for the custom-design of polymer and phase-change composites with targeted thermal performances.

Considering critical building materials for embodied carbon emissions in buildings: A machine learning-based prediction model and tool

Construction activities discharge considerable carbon emissions, causing serious environmental problems and gaining increasing attention. For the large-scale construction area, high emission intensity, and significant carbon reduction potential, embodied carbon emissions of buildings worth special studying. However, previous studies are usually post-evaluation and ignore the influences of project, construction and management. This paper focuses on critical building materials and adopts machine learning methods to realize carbon prediction at design stage. The activity data, including critical building materials, water, and energy consumption, is analyzed and 30 influencing factors at the project, construction, and management levels are identified. Three algorithms (artificial neural network, support vector regression and extreme gradient boosting) are used to develop machine learning models. The proposed methodology is applied to 70 projects in the Yangtze River Delta region of China. Results show that the established models achieved high interpretability (R2 >0.7) and small average error (5.33%), well proving theirs feasibility. Furthermore, an automated tool is developed to assist practitioners to predict the critical materials consumption and embodied carbon emissions conveniently. The proposed operable model and practical tool can efficiently support effective adjustments and improvement to reduce carbon in construction.

Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

The present study utilized machine learning (ML) techniques to investigate the effects of eggshell powder (ESP) and recycled glass powder (RGP) on cement composites subjected to an acidic setting. A dataset acquired from the published literature was employed to develop machine learning-based predictive models for the cement mortar’s compressive strength (CS) decrease. Artificial neural network (ANN), K-nearest neighbor (KNN), and linear regression (LR) were chosen for modeling. Also, RreliefF analysis was performed to study the relevance of variables. A total of 234 data points were utilized to train/test ML algorithms. Cement, sand, water, silica fume, superplasticizer, glass powder, eggshell powder, and 90 days of CS were considered as input variables. The outcomes of the research showed that the employed models could be applied to evaluate the reduction percentage of CS in cement composites, including ESP and RGP, after being exposed to acid. Based on the R2 values (0.87 for the ANN, 0.81 for the KNN, and 0.78 for LR), as well as the assessment of variation between test values and anticipated outcomes and errors (1.32% for ANN, 1.57% for KNN, and 1.69% for LR), it was determined that the accuracy of the ANN model was superior to the KNN and LR. The sieve diagram exhibited a correlation amongst the model predicted and target results. The outcomes of the RreliefF analysis suggested that ESP and RGP significantly influenced the CS loss of samples with RreliefF scores of 0.26 and 0.21, respectively. Based on the outcomes of the research, the ANN approach was determined suitable for predicting the CS loss of mortar subjected to acidic environments, thereby eliminating lab testing trails.

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.