Meta Model Research Articles

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy.

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

A novel Tree-augmented Bayesian network for predicting rock weathering degree using incomplete dataset

The precise forecasting of the weathering degree of surrounding rock holds paramount importance for the scientific design and secure execution of tunnel engineering. The apparent features of the surrounding rock serve as critical indicators for evaluating its weathering degree. This paper endeavors to quantify the rock apparent features based on an improved Computer vision model and establish a multi-source heterogeneous dataset encompassing 10 parameters, thereby facilitating data-driven predictions of the weathering degree. Specifically, the rock appearance parameters are quantified and segmented by an improved Tunnel face feature segmentation (TFFSeg) model, which is tailored to the unique characteristics of groundwater, fractures, and interlayers. Concurrently, the TFFSeg model exhibits significantly enhanced performance for these rock features compared to other widely employed Computer vision methods. Subsequently, this multi-source dataset is further enriched by incorporating rock physical and mechanical parameters as well as tunnel design parameters. Nevertheless, the issue of data incompleteness persists within this dataset. To achieve precise prediction of the weathering degree based on this incomplete dataset, a novel Tree-augmented Bayesian network (TAN-BN) is designed, which is capable of learning from incomplete datasets. The predictive outcomes demonstrate that the proposed TAN-BN surpasses other currently utilized meta models and ensemble models, such as ANN, GBRT, and Naive BN. Finally, sensitivity analysis is conducted to determine the importance rankings of the 10 parameters, offering valuable insights for on-site evaluation of the rock weathering degree at the tunnel face.

Enhancement of joint quality for laser welded dissimilar material cell-to-busbar joints using meta model-based multi-objective optimization

In the battery pack assembly, it is essential to ensure that the cell-to-busbar joints can be produced with high quality and with minimal impact on the individual battery cells. This study examines the influence of process parameters on the joint quality for nickel-plated copper and steel plates, laser welded in an overlap configuration. Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. In an meta model-based multi-objective optimization approach, the non-dominated sorting genetic algorithm II (NSGA-II) is used to efficiently search for trade-off solutions and the meta models are used for objective approximation. As a result, the objective evaluation time is decreased from around 9 h, when evaluated directly from numerical simulations, to only tenths of a second. From the Pareto-optimal front of trade-off solutions, three optimal solutions are selected for validation. The selected solutions are validated through laser welding experiments and numerical simulations, resulting in joints with large interface widths and low in-process temperatures without a full penetration.

Changes in University Students’ Behaviour and Study Burnout Risk During ACT-based Online Course Intervention: A Mixed Methods Study

Previous research in higher education has shown that Acceptance and Commitment Therapy (ACT) -based interventions have beneficial effects on students’ well-being. However, not much is known about the range of behaviour changes behind these outcomes.In this mixed methods study, we explored the variety of the changes ACT-based course participating university students (N=101) describe in their reflective learning reports. We did so by applying the Extended Evolutionary Meta Model (EEMM) in abductive content analysis, and by applying network visualisation of the various study burnout risk change groups.The results showed that the largest proportion of students described changes in the EEMM dimension of overt behaviour (75%), especially time and effort management. Vitality, and decreased distress were the most described outcomes related to behaviour changes.Study burnout risk decreased significantly at the course level. When observing the interrelations of behaviour changes in the networks of burnout risk change groups, alongside time and effort management, attentional and affective dimensions representing present moment awareness and acceptance were central changes in the group that had the greatest reduction in burnout risk. These findings contribute to the need to map out the variety of beneficial mechanisms and serve further idiographic research about changes during study-integrated ACT-based interventions.

Using the Super-learner to Predict the Chemical Acute Toxicity on Rats

With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD50) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relationship models relying on single algorithm always lack interpretability and precision, given the complexity of the mechanisms underlying acute toxicity. To address these issues, this study has developed a predictive framework using an ensemble learning model based on Super-learner. Particularly, we first obtained LD50 data for 9,843 compounds and constructed 16 meta models using 4 molecular descriptors and machine learning algorithms. The Super-learner model performed well, achieving R² values of 0.61 and 0.64 in five-fold cross-validation and test sets, respectively, with corresponding root mean square errors of 0.55 and 0.64, significantly outperforming the results of individual model. Additionally, we incorporated data filtering and applicability domain methods, which demonstrated that the Super-learner can mitigate the impact of dataset noise to some extent. The model achieved an R² of 0.76 within an applicability domain, ensuring prediction accuracy within the chemical space. Compared to previous studies, the model developed here using Super-learner generally achieved better performance across a larger applicability domain. Finally, we has launched an online tool (http://sltox.hhra.net), allowing users to quickly predict LD50 of compounds, greatly simplifying the chemical safety assessment process. This study not only provides an effective and cost-efficient method for predicting chemical toxicity but also offers technical support and data for risk assessments of chemicals.

Semi-supervised meta-learning elucidates understudied molecular interactions

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We develop a deep learning framework—Meta Model Agnostic Pseudo Label Learning (MMAPLE)—to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when conventional transfer learning fails. The uniqueness of MMAPLE is to integrate the concept of meta-learning, transfer learning and semi-supervised learning into a unified framework. The power of MMAPLE is demonstrated in three applications in an OOD setting where chemicals or proteins in unseen data are dramatically different from those in training data: predicting drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over various base models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by activity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.

Stacking Ensemble Artificial Intelligence Model for Heart Disease Diagnosis

Personalized treatment plans, predictive analytics, and artificial intelligence (AI)-driven diagnostics are becoming more and more popular as a way to improve decision-making, expedite operations, and improve patient care. But there are still a number of substantial barriers to overcome, which includes but not limited to issues with user adoption, trust, prejudice, and fairness brought on by resistance from healthcare providers and a lack of confidence in the system's recommendations. To overcome these challenges and realize the full potential of AI-driven solutions, the system's accuracy and safety through meticulous testing and validation of AI algorithms becomes indispensable. This research provides a hybrid AI model that blends three base models with a Meta model in order to diagnose heart disease effectively. The essence is to revalidate the existing AI diagnostic models for cardiac diseases diagnostics and to tackle the concerns impeding the full utilization of the available AI diagnostic system. The study makes use of each model's advantages by merging these various and complimentary algorithms into a stacking ensemble model to create a diagnostic system that is more potent. Using publicly available heart disease data, the model performs remarkably well; it achieves 89% accuracy, 85% recall (sensitivity), 92% specificity, and 89% precision. This hybrid model's performance and proven efficacy are expected to boost trust in the system's recommendations and encourage broader implementation in clinical practice.

Distributed design optimization of multi-component systems using meta models and topology optimization

Distributed optimization architectures decompose large monolithic optimization problems into sets of smaller and more manageable optimization subproblems. To ensure consistency and convergence towards a global optimum, however, cumbersome coordination is necessary and often not sufficient. A distributed optimization architecture was previously proposed that does not require coordination. This so-called Informed Decomposition is based on two types of optimization problems: (1) one for system optimization to produce stiffness requirements on components using pre-trained meta models and (2) one for the optimization of components with two interfaces to produce detailed geometries that satisfy the stiffness requirements. Each component optimization problem can be carried out independently and in parallel. This paper extends the approach to three-dimensional structures consisting of components with six degrees of freedom per interface, thus significantly increasing the applicability to practical problems. For this, an 8-dimensional representation of the general 12 x 12 interface stiffness matrix for components is derived. Meta models for mass estimation and physical feasibility of stiffness targets are trained using an active-learning strategy. A simple two-component structure and a large robot structure consisting of four components subject to constraints for 100 different loading scenarios are optimized. The example results are at most 12.9% heavier than those of a monolithic optimization.

Conflict Management through Neuro-Linguistic Programming (NLP) Approach

Conflict is an inevitable part of life. Conflict management is an essential part of the success to manage interacting issues with others, both in professional and personal environments. One highly suitable method for conflict management is Neuro-Linguistic Programming (NLP) approach, which views human language and behavior as learnable and changeable to achieve specific goals, including resolving conflicts. NLP provides several techniques and methods that can help someone manage conflict more effectively, such as managing emotions, identifying and understanding differences in perception, and building better relationships. This study aims to test the effectiveness of the NLP approach in conflict management through a case study that is researched using a qualitative approach with interview techniques to collect data from sources involved directly or indirectly in the conflict that occurred. The results of the study show that the NLP approach is considered effective in social conflict management. NLP techniques in recognizing communication patterns through Linguistic Model (Meta Model and Milton Model) using Chunking techniques can help manage conflict and improve relationships between conflicting parties. This study also found that the Chunking method in NLP is very suitable for resolving conflicts at the micro level, while for conflict resolution at the meso and macro levels, the Chunking approach can be combined with other NLP approaches such as ‘ecology checking’, and ‘future pacing’. Overall, conflict management using NLP can help improve someone's ability, especially for conflict resolvers and mediators, to manage conflicts more effectively and help conflicting parties find fair and equal solutions.

A novel decision support system for designing fixed shading systems in the early design stage: A case study in Egypt

Solar shading is a crucial element that can significantly impact lighting conditions and energy consumption, especially in hot regions. However, selecting the best shading design that achieves optimal daylight performance while meeting architects' requirements is very challenging. This is because it involves considering many design parameters and balancing multiple objectives. The paper introduces a decision support system for designing fixed shading systems to enhance interior environments and reduce energy consumption. Parametric simulation, a meta-model based on automatic selection of regression machine learning algorithms (RMLA), and non-dominated sorting genetic algorithm II (NSGA-II) are used to prepare the designs for selection. Three multiple criteria decision-making (MCDM) methods are employed: the analytic hierarchy process (AHP), entropy, and the technique for order preference by similarity to the ideal solution (TOPSIS) in two combinations (AHP-TOPSIS and entropy-TOPSIS) to adapt to different expert availability scenarios. The proposed method is coded and automated with an easy-to-use interactive interface for flexibility and easy application. The method is applied to help design a fixed shading system for hot climate regions in Cairo, Egypt, which involves considering five objectives: useful day light illuminance (UDI), energy use intensity (EUI), solar gains (SG), daylight autonomy (DA), and continuous daylight autonomy (CDA). The proposed method can provide an efficient design that significantly improves daylight performance. Different dataset sizes are used for training the meta model to study their effect on the final selected design. A sensitivity analysis is done to explore the impact of changes in experts’ opinions on the decision-making process.

Trace Semantics for C++11 Memory Model

The C and C++ languages introduced the relaxed-memory concurrency into the language specification for efficiency purposes in 2011. Trace semantics can provide the mathematical foundation for the proposed C++11 memory model, and there is a lack of investigation of trace semantics for C++11. The Promising Semantics (PS) of Kang et al. provides the standard SC-style operational semantics for the C++11 concurrency model, where “SC” refers to “Sequential Consistency”. Inspired by PS, in this paper we first investigate the trace semantics for the relaxed read and write accesses under C++11, acting in the denotational semantics style. In our semantic model, a trace is in the form of a sequence of snapshots, and the snapshots record the modification in the relevant global or local variables, and the thread view. Moreover, the trace semantics for the release/acquire accesses under C++11 is also explored, based on the separated thread views and newly added message views. When considering this trace model, different accesses bring in their unique snapshots, and make distinguished effects on the production of the sequences. For any given program, the proposed trace semantics in this paper produces all the valid traces directly. Further, our trace semantics, together with that for TSO and MCA ARMv8, has the possibility to be the foundation of the meta model of the trace semantics for weak memory models.

Computational Analysis of Predicting Stress Intensity Factor Using Hdmr Technique

The present research focused to develop meta-model (MM) as a low-cost substitute for the Finite Element Method (FEM), to forecast the Stress Intensity Factor (SIF) of a semi-elliptic crack propagating. Multi-linear regression (MLR) based MM is developed for predicting the crack propagation. Three parameters—Root Mean Square Error (RMSE), Maximum Absolute Error (AAE), and Average Absolute Error—are examined to compare the MM's precision (AAE).

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.

Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

Insights into Bitcoin and energy nexus. A Bitcoin price prediction in bull and bear markets using a complex meta model and SQL analytical functions

Cryptocurrencies are in the center of attention of investors, public authorities and researchers, but the interest has shifted from purely financial aspects regarding the way of trading, lack of regulation and supervision of transactions, volatility, correlation with other assets to aspects related to sustainability taking in account the high energy consumption generated by the mining process and the impact on environmental pollution. Bitcoin was chosen for the research considering the dominance that this financial asset has on the cryptocurrency market and its position as alpha currency.The article focuses on the relationship between Bitcoin transactions and energy consumption, for period 1st January 2019—31st of May 2022, this interval having significant price movements. The authors made a prediction of the Bitcoin price using a complex meta-model and SQL analytical functions. The analysis is based on 15 fundamental variables in order to forecast the price: Bitcoin data (prices and volume), electricity price and traded quantity on day-ahead market (DAM), gas price and traded quantity on DAM, inflation in EU, EU-ETS emissions certificates and oil prices. The study reveals the importance of the relationship Bitcoin—energy—carbon emissions, elements that capture the impact of the mining process on the environment from the perspective of energy consumption. Investors on the Bitcoin market must be aware not only of the importance of financial aspects on the price of cryptocurrencies (inflation, demand, offer), but also of other elements related to the evolution of energy prices (electricity, oil, gas, renewable energy) and the evolution of emissions certificates prices. Considering the promotion of the principles of sustainable development on the capital market, portfolio investors have become increasingly attentive to the social and environmental performance of financial assets. This study aims to make financial market players aware of the non-financial implications of their transactions. In addition, the energy transition and the reconfiguration of the energy mix are elements of impact on the cryptocurrency market through the technical levers involved in the mining process.

Loss of Life Transformer Prediction Based on Stacking Ensemble Improved by Genetic Algorithm By IJISRT

Prediction for loss of life transfomer is very important to ensure the reliability and efficiency of the power system. In this paper, an innovative model is proposed to improve the accuracy of lost of life transfomer prediction using stacking ensembles enhanced with genetic algorithm (GA). The aim is to develop a robust model to estimate the remaining life of a transformer in order to generally increase the reliability of the electrical energy distribution system. This approach involves integrating various machine learning models as a basic model, namely Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). A stacking ensemble framework is then used to combine the predictions of these base models using a meta model namely Logistic Regression (LR). The results show a significant improvement in both transformers using stacking-GA, both TR-A and TR-B, with each prediction evaluation 99% and with a minimal error rate, namely approaching 0.the developed framework presents a promising solution for accurate and reliable transformer life prediction. By integrating a variety of basic models, applying improved stacking layouts using GA, these models offer valuable insights to improve maintenance strategies and system reliability in power grids.

Modelling the propagation of properties across services in cyber-physical energy systems

Modern power systems, referred to as cyber-physical energy systems (CPESs), are complex systems with strong interdependencies between power and information and communication technology (ICT) systems. CPESs also have dependencies between the essential grid services. For instance, coordinated voltage control depends on state estimation, which depends on measurement acquisition. Since the operation of CPESs is largely influenced by these grid services, assessing their performance is crucial for assessing the performance of a CPES. Most of these grid services are enabled by the ICT system, i.e., they rely to a high degree on ICT. Hence, properties such as availability, correctness and timeliness, which depend on the involved software, hardware and data of the ICT system, must be considered for assessing the performance of an ICT-enabled grid service. Disturbances and repairs in CPESs impact these properties, which can then propagate and affect the performance of a grid service as well as other dependent grid services. There is, therefore, a need to model the influence of the properties of software, hardware and data on ICT-enabled grid services for single services as well as across several services, resulting in a propagation of these parameters. Current literature lacks such a model, which can used not only to investigate but also to visualise the impact of these properties on the overall perfromance of a grid service as well as other dependent grid services. This paper proposes a meta model for assessing the performance of ICT-enabled grid services, which can be instantiated for different grid services considering their dependencies. A multi-dimensional operational state space, which serves as a visualisation of the performance of grid services in terms of their state trajectory, is also proposed in this paper. The contributions are then demonstrated by a case study with a state estimation service and the widely-used CIGRE medium voltage benchmark power grid augmented with an ICT system. Three scenarios with disturbances are presented to show the benefits of the contributions. Specifically, the performance of the state estimation service considering the disturbances is investigated using the meta model, and the change in performance is visualised as trajectories using the operational state space. These contributions enable new possibilities for planning and vulnerability analyses: property changes in parts of the ICT system can be simulated to investigate their consequences throughout the ICT-enabled grid services. A trajectory representing their performance can then be visualized in the state space based on which measures could be implemented to potentially improve the resilience of the service against the considered disturbances.

Meta model-based and cross entropy-based importance sampling algorithms for efficiently solving system failure probability function

The multi-mode system failure probability function (SFPF) can quantify how the distribution parameters of the random input vector affect the system safety and decouple the system reliability-based design optimization model. However, for a problem with a time-consuming implicit performance function and rare failure domain, efficiently solving the SFPF remains significantly challenging. Therefore, in this study, two efficient algorithms are proposed, namely, the meta model-based importance sampling and cross entropy-based importance sampling. The contributions of this study are twofold. The first is constructing a single-loop optimal importance sampling density (SL-OISD) method to decouple the double-loop framework for analyzing the SFPF. The second is establishing two methods to efficiently approximate the SL-OISD and complete the SFPF estimation. The first method is based on the meta model of the system performance function, which is abbreviated as SL-Meta-IS. The second method is based on minimizing the cross entropy between the Gaussian mixture density model and SL-OISD, which is abbreviated as SL-CE-IS. To reduce the number of evaluating the system performance function when approximating the SL-OISD, sampling the SL-OISD, and identifying the state of the samples for completing the SFPF estimation, an adaptive Kriging model of the system performance function is introduced into SL-Meta-IS and SL-CE-IS. Owing to decoupling the double-loop framework into a single-loop framework, replacing the time-consuming system performance function with the economic Kriging model, and employing importance sampling variance reduction techniques to address issues related to the rare failure domain, the proposed SL-Meta-IS and SL-CE-IS methods greatly enhance the efficiency of SFPF estimations. The numerical and practical examples demonstrate that the two proposed methods are superior to the existing algorithms; moreover, the efficiency of SL-CE-IS is higher than that of SL-Meta-IS.

Improved playstore review sentiment classification accuracy with stacking ensemble

In today's digital era, user reviews on the Playstore platform are an invaluable source of information for developers, offering insights that are critical for service improvement. Previous research has explored the application of stacking ensemble methods, such as in the context of predicting depression among university students, to enhance prediction accuracy. However, these studies often do not explicitly detail the data acquisition process, leaving a gap in understanding the applicability of these methods to different domains. This research aims to bridge this gap by applying the stacking ensemble approach to improve the accuracy of sentiment classification in Playstore reviews, with a clear exposition of the data collection method. Utilizing Logistic Regression as the meta classifier, this methodology is executed in several stages. Initially, data was collected from user reviews of online loan applications on Google Playstore, ensuring transparency in the data acquisition process. The data is then classified using three basic models: Random Forest, Naive Bayes, and SVM. The outputs of these models serve as inputs to the Logistic Regression meta model. A comparison of each base model output with the meta model was subsequently carried out. The test results on the Playstore review dataset demonstrated an increase in accuracy, precision, recall, and F1 score compared to using a single model, achieving an accuracy of 87.05%, which surpasses Random Forest (85.6%), Naive Bayes (85.55%), and SVM (86.5%). This indicates the effectiveness of the stacking ensemble method in providing deeper and more accurate insights into user sentiment, overcoming the limitations of single models and previous research by explicitly addressing data acquisition methods.

Stacked ensemble learning approach for PCM-based double-pipe latent heat thermal energy storage prediction towards flexible building energy

Dynamic performance prediction of the PCM-based double-pipe latent heat thermal energy storage (LHTES) system plays an important role for building peak load shifting. This study presents a stacked ensemble learning framework for the phase change performance prediction of LHTES systems. The proposed model has a two-layer structure involving base and meta models: Regression Tree, Support Vector Regression and Linear Regression. Sensitivity analysis has been utilized for the feature extraction considering possible geometrical and physical parameters of LHTES systems with a fixed PCM volume that can store 0.94 kWh of energy within 255 min. Case studies show that the proposed model outperforms the base models in terms of both prediction accuracy and robustness. Compared with base models, the proposed model has a maximum 7.82% (charging) enhancement in Mean Absolute Percentage Error (MAPE) over all-range data sets and a maximum 16.43% enhancement in MAPE for the initial discharging stage, and has more stable predictive results within the high accuracy zone (accuracy > 95%) with a maximum 7.3% (charging) increase in the distribution density over all-range data sets and a maximum interquartile range reduction of 25.6% (middle discharging stage) in Mean Absolute Error. Application of the proposed model achieved more building peak load reduction.

Forecasting Survival Rates in Metastatic Colorectal Cancer Patients Undergoing Bevacizumab-Based Chemotherapy: A Machine Learning Approach

Background: Antibiotics can play a pivotal role in the treatment of colorectal cancer (CRC) at various stages of the disease, both directly and indirectly. Identifying novel patterns of antibiotic effects or responses in CRC within extensive medical data poses a significant challenge that can be addressed through algorithmic approaches. Machine Learning (ML) emerges as a promising solution for predicting clinical outcomes using clinical and heterogeneous cancer data. In the pursuit of our objective, we employed ML techniques for predicting CRC mortality and antibiotic influence. Methods: We utilized a dataset to examine the accuracy of death prediction in metastatic colorectal cancer. In addition, we analyzed the association between antibiotic exposure and mortality in metastatic colorectal cancer. The dataset comprised 147 patients, nineteen independent variables, and one dependent variable. Our analysis involved testing different classification-supervised ML, including an oversampling pool for classification models, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machine, Random Forest, XGBboost Classifier, a consensus of all models, and a consensus of top models (meta models). Results: The consensus of the top models’ classifier exhibited the highest accuracy among the algorithms tested (93%). This model met the standards for good accuracy, surpassing the 90% threshold considered useful in ML applications. Consistent with the accuracy results, other metrics are also good, including precision (0.96), recall (0.93), F-Beta (0.94), and AUC (0.93). Hazard ratio analysis suggests that there is no discernible difference between patients who received antibiotics and those who did not. Conclusions: Our modelling approach provides an alternative for analyzing and predicting the relationship between antibiotics and mortality in metastatic colorectal cancer patients treated with bevacizumab, complementing classic statistical methods. This methodology lays the groundwork for future use of datasets in cancer treatment research and highlights the advantages of meta models.