Ensemble Learning Techniques Research Articles

Ensemble learning approach for distinguishing human and computer-generated Arabic reviews

While customer reviews are crucial for businesses to maintain their standing in the marketplace, some may employ humans to create favorable reviews for their benefit. However, advances in artificial intelligence have made it less complex to create these reviews, which now rival real ones written by humans. This poses a significant challenge in distinguishing between genuine and artificially generated reviews, thereby impacting consumer trust and decision-making processes. Research has been conducted to classify whether English reviews were authored by humans or computers. However, there is a notable scarcity of similar studies conducted in Arabic. Moreover, the potential of ensemble learning (EL) techniques, such as soft voting, to enhance model performance remains underexplored. This study conducts a comprehensive empirical analysis using various models, including traditional machine learning, deep learning, and transformers, with an investigation into ensemble techniques, like soft voting, to classify human and computer-generated Arabic reviews. Integrating top logistic regression (LR) and convolutional neural network (CNN) models, it achieves an accuracy of 89.70%, akin to AraBERT’s 90.0%. Additionally, a thorough textual analysis, covering parts of speech (POS), emotions, and linguistics reveals significant linguistic disparities between human and computer-generated reviews. Notably, computer-generated reviews exhibit a substantially higher proportion of adjectives (6.3%) compared to human reviews (0.46%), providing crucial insights for discerning between the two review types. The results not only advance natural language processing (NLP) in Arabic but also have significant implications for businesses combating the influence of fake reviews on consumer trust and decision-making.

Screening of Key Transcripts from Expression Data Using Applied Artificial Intelligence for Cancer Prediction

Research on the effects of artificial intelligence (AI)-driven solutions in the field of oncology is still being conducted all around the world. Applications of AI to identify the transcripts that cause cancer are being investigated, particularly when employing ensemble learning techniques. Ensemble feature fusion is the process of distributing the feature selection process and combining the local features that were chosen to create a smaller global feature set. This article addresses the use of ensemble feature fusion in the field of differential transcript expression analysis to screen for important transcripts linked to a disease. Owing to the necessity and significance of research in cancer diagnosis, an ensemble feature fusion approach experimental case study has been carried out using 109 liver cancer samples obtained from Mitranscriptome dataset. It was found that the expression data were used to filter the most pertinent transcripts, which allowed for the best possible differentiation between sample type. Accordingly, a set of 26 significant liver cancer causing transcripts has been screened using unanimous voting-scheme, giving an accuracy of percentage of 96. During generalization testing, cancer prediction classifiers constructed with this essential transcript collection shown excellent discriminating power and performed well in differentiating between normal and malignant cells. By resolving the “high dimension-low sample (High p Low n)” issue that is typically present in the expression data, this improves the predicting ability of cancer diagnostic systems. In the field of oncology, artificial intelligence-powered solutions will facilitate the development of applications that prioritize Sustainable Development Goal 3: Good Health and Well-Being.

Discovering the underground coal mining accident patterns in Spain from 2003 to 2021: Insights through machine learning techniques

The safety of underground coal mining has always been a global concern, involving the stable supply of energy and stakes in miners’ lives. Lessons learned from historical accidents and transforming into practical experience help reduce the quantity and severity of accidents. In this study, six ensemble learning techniques, including AdaBoost, Extra Trees, GBDT, LightGBM, Random Forest, and XGBoost, were used to investigate the correlation between accident-causing factors and severity. Firstly, 39487 underground coal mine accidents data was obtained from Spain, variables were categorized and coded. To address the extreme class imbalance, a new dataset (2468 cases) was obtained by data sampling from the original database. Subsequently, the new dataset was randomly divided into training sets (75% of the data) and test sets (25% of the data), then the hyperparameters of each model were optimized and configured. Thirdly, the models’ performance was evaluated on the test data by five metrics (accuracy, Cohen’s Kappa, precision, recall, and F1). Finally, accident patterns were derived from the identified variables along with preventive strategies. Results show that tree-based ensemble learning model performs better compared to the boosting model, and the relative importance of seven variables were determined, where previous cause (PC) and material agent (MA) are the most important factors, followed by the miner’s physical activity (PA), age (A), and experience (E), scale (S) and preventive organization (PO) are in the third tier. Furthermore, the type of accident and injury caused by PC were confirmed. Working with hand tools, younger age, lack of experience, small-scale coal mines, and unfit preventive organization increased the risk of accidents. This study not only facilitates the prediction of accident severity but also provides strategies for preventing and mitigating accidents.

A Comprehensive Review of SCADA-Based Wind Turbine Performance and Reliability Modeling with Machine Learning Approaches

The increasing reliance on wind energy to meet global energy demands has made wind turbine performance optimization and reliability a critical area of research. Supervisory Control and Data Acquisition (SCADA) data, which provides real-time operational insights into wind turbines, plays a pivotal role in predictive maintenance, fault detection, and energy output optimization. This review explores the current methodologies and advancements in wind turbine performance modeling and reliability analysis, with a particular emphasis on machine learning (ML) approaches. Existing studies that utilize SCADA data to implement various ML models, such as decision trees, neural networks, and ensemble learning techniques (bagging, boosting, stacking), are analyzed for their effectiveness in predicting turbine failures, improving energy efficiency, and optimizing maintenance schedules. Key findings from multiple studies are synthesized, highlighting the strengths, limitations, and real-world applications of these models. Challenges in data quality, model generalization, and the implementation of real-time ML-driven systems in wind farms are also addressed. This review aims to provide a comprehensive overview of the current state of SCADA-based wind turbine analysis and to offer a roadmap for future research that bridges the gap between data-driven models and their practical deployment in wind energy systems.

Ensemble learning using Gompertz function for leukemia classification

Leukemia, a life-threatening disease primarily affecting children below 15 and individuals aged 55 and above, is characterized by the overproduction of abnormal blood cells. Timely detection of leukemia plays a crucial role in significantly reducing the associated mortality rate. Given the recent developments in deep learning, the development of automated leukemia detection techniques using blood smear images has emerged as an important research area. This manuscript introduces a novel algorithm to classify microscopic blood smear images into five categories: AML, CML, ALL, CLL and normal. The study investigates the use of pre-trained convolutional neural networks powered by a fuzzy ensemble-based classifier. The fuzzy-based ensemble learning technique employs adaptable weights according to the confidence scores derived from the base learner models instead of using fixed weights for the base learner models. Gompertz function with parameters, α, β and γ are considered here. The parameters of the Gompertz function are further optimized using a grid search approach to achieve the most favorable classification outcomes. Conducting a 5-class classification, the proposed algorithm achieved an accuracy of 88.80%, surpassing the performance of traditional ensemble techniques such as average, weighted average and majority voting by a significant margin of 1.83%, 1.83% and 5.71%, respectively.

Conventional Machine Learning and Ensemble Learning Techniques in Cardiovascular Disease Prediction and Analysis

Cardiovascular diseases, which significantly affect the heart and blood vessels, are one of the leading causes of death worldwide. Early diagnosis and treatment of these diseases, which cause approximately 19.1 million deaths, are essential. Many problems, such as coronary artery disease, blood vessel disease, irregular heartbeat, heart muscle disease, heart valve problems, and congenital heart defects, are included in this disease definition. Today, researchers in the field of cardiovascular disease are using approaches based on diagnosis-oriented machine learning. In this study, feature extraction is performed for the detection of cardiovascular disease, and classification processes are performed with a Support Vector Machine, Naive Bayes, Decision Tree, K-Nearest Neighbor, Bagging Classifier, Random Forest, Gradient Boosting, Logistic Regression, AdaBoost, Linear Discriminant Analysis and Artificial Neural Networks methods. A total of 918 observations from Cleveland, Hungarian Institute of Cardiology, University Hospitals of Switzerland, and Zurich, VA Medical Center were included in the study. Principal Component Analysis, a dimensionality reduction method, was used to reduce the number of features in the dataset. In the experimental findings, feature increase with artificial variables was also performed and used in the classifiers in addition to feature reduction. Support Vector Machines, Decision Trees, Grid Search Cross Validation, and existing various Bagging and Boosting techniques have been used to improve algorithm performance in disease classification. Gaussian Naïve Bayes was the highest-performing algorithm among the compared methods, with 91.0% accuracy on a weighted average basis as a result of a 3.0% improvement.

Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays

Accurate prediction of the compression index (cc) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining cc are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the cc of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e0), and natural water content w—were leveraged for accurate cc prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination (R2) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with e0 identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments.

Addressing data sparsity and cold-start challenges in recommender systems using advanced deep learning and self-supervised learning techniques

ABSTRACT The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS.

Cancer detection with various classification models: A comprehensive feature analysis using HMM to extract a nucleotide pattern

This work presents a novel feature extraction method for identifying complex patterns in genomic sequences by employing the Hidden Markov Model (HMM). In this study, we use HMM to identify gene nucleotide patterns that are specific to malignant and non-malignant cells. Crucial genetic components DNA and RNA are involved in many biological processes that impact both healthy and malignant cells. Early patient identification is essential to successful cancer diagnosis and therapy. Varying nucleotide patterns indicate different cellular responses, which are important to understanding the molecular causes of cancer and associated disorders. We present a detailed study of nucleotide patterns in whole raw nucleotide sequences with variations in both protein sequence (CDS) and non-protein sequence (NCDS) in both malignant and non-malignant cells. Nucleotide prediction has been achieved while computational expenses are reduced by using the proposed HMM for feature extraction and selection. The classification models implemented in this work for cancer detection are Gradient-Boosted Decision Trees (GBDT), Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM) with kernels. The suggested classification model's accuracy and 10-fold cross-validation have been validated via comprehensive case studies. The results reveal that DT and ensemble learning techniques significantly differentiate between malignant and non-malignant DNA sequences. SVM with suitable kernels improves cancer detection accuracy significantly. Combining feature reduction approaches with nucleotide pattern classifiers based on Hidden Markov models improves performance and ensures reliable cancer detection.

Interpretable machine learning methods to predict the mechanical properties of ABX3 perovskites

This paper proposes the utility of interpretable ensemble learning models for predicting the mechanical properties (bulk, shear and Young moduli) of ABX3 perovskite compounds with the A, B, and X referring to the 3 elements that make the cubic 3-dimensional framework of the perovskite compounds. These models consist of 3 ensemble learning techniques namely CatBoost, Random Forest, and XGBoost. To expand the feature space, robust first-principles density functional theory calculations were used to generate some of the input features, namely elastic constants, density, volume per atom, and ground state energy per atom. The order of the input feature ranking that influences the machine learning (ML) model decisions was then determined. For this, we performed correlation analysis on the multi-dimensional input feature space, suppressed features with high collinearity, and selected features with limited correlation. We trained the three ensemble learning techniques on the desired vectorial input feature representation to predict the mechanical properties. Furthermore, we employed the Shapley Additive Explanations (SHAP) algorithm for analysing the intrinsic decision-making rationality of the ensemble learning models. We measured the performance in the context of the error metrics and coefficient of determination, R2. The results show that XGBoost outperforms other approaches when predicting the shear modulus or Young modulus of the perovskite compounds yielding the least error metrics and the highest R2 value (0.97) in the testing phase. However, both CatBoost and Random Forest outperformed XGBoost when attempting to predict the bulk modulus in the testing phase. The deficiency of the XGBoost in predicting the bulk modulus can be ascribed to an overfitting problem which can occur when the ML model gives accurate predictions for training data but not for test data. Furthermore, the SHAP algorithm provides an insight into the order of feature importance (from highest to lowest). Additionally, we conducted a post-analysis using a holistic ranking to analyse the relative importance of the SHAP feature impact comprehension for the examined ensemble learning techniques. Our findings indicate that the elastic constants are the most important input features influencing the predictive decision of the ensemble learning models.

LSTM-Autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.

Global decline in microbial-derived carbon stocks with climate warming and its future projections

Abstract Soil organic carbon (SOC) represents the largest terrestrial pool of organic carbon and is indispensable for mitigating climate change and sustaining soil fertility. As a major component of stable SOC, microbial-derived carbon (MDC) accounts for approximately half of the total SOC and has repercussions on climate feedback. However, our understanding of the spatial and temporal dynamics of MDC stocks is limited, hindering assessments of the long-term impacts of global warming on persistent SOC sequestration in the soil‒atmosphere C cycle. Here, we compiled an extensive global dataset and employed ensemble machine learning techniques to forecast the spatial-temporal dynamics of MDC stocks across 93.4% of the total global land area from 1981 to 2018. Our findings revealed that for every 1°C increase in temperature, there was a global decrease of 6.7 Pg in the soil MDC stock within the predictable areas, equivalent to 1.4% of the total MDC stock or 0.9% of the atmospheric C pool. The tropical regions experienced the most substantial declines in MDC stocks. We further projected future MDC stocks for the next century based on shared socioeconomic pathways, showing a global decline in MDC stocks with a potential 6–37 Pg reduction by 2100 depending on future pathways. We recommend integrating the response of MDC stocks to warming into socioeconomic models to enhance confidence in selecting sustainable pathways.

Improving Hate Speech Classification Through Ensemble Learning and Explainable AI Techniques

Improving Hate Speech Classification Through Ensemble Learning and Explainable AI Techniques

Attention-Driven Transfer Learning Model for Improved IoT Intrusion Detection

The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network’s integrity and resilience. This study proposes an Intrusion Detection System (IDS) for IoT environments that leverages Transfer Learning (TL) and the Convolutional Block Attention Module (CBAM). We extensively evaluate four prominent pre-trained models, each integrated with an independent CBAM at the uppermost layer. Our methodology is validated using the BoT-IoT dataset, which undergoes preprocessing to rectify the imbalanced data distribution, eliminate redundancy, and reduce dimensionality. Subsequently, the tabular dataset is transformed into RGB images to enhance the interpretation of complex patterns. Our evaluation results demonstrate that integrating TL models with the CBAM significantly improves classification accuracy and reduces false-positive rates. Additionally, to further enhance the system performance, we employ an Ensemble Learning (EL) technique to aggregate predictions from the two best-performing models. The final findings prove that our TL-CBAM-EL model achieves superior performance, attaining an accuracy of 99.93% as well as high recall, precision, and F1-score. Henceforth, the proposed IDS is a robust and efficient solution for securing IoT networks.

Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies.In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Enhancing IoT network defense: advanced intrusion detection via ensemble learning techniques

The Internet of Things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our framework uses the ‘BoT-IoT’ and ‘TON-IoT’ datasets, which include various IoT network traffic and cyber-attack scenarios, such as DDoS and data infiltration, to train machine learning and ensemble models. Specifically, it combines three machine learning models-decision tree, resilient backpropagation (RProp) multilayer perceptron (MLP), and logistic regression-into ensemble methods like voting and stacking to improve prediction accuracy and reduce detection errors. These ensemble classifiers outperform individual models, demonstrating the benefit of diverse learning techniques. Our framework achieves high accuracy, with 99.99% for binary classification on the BoT-IoT dataset and 97.31% on the ToN-IoT dataset. For multiclass classification, it achieves 99.99% on BoT-IoT and 96.32% on ToN-IoT, significantly enhancing IDS effectiveness against IoT cybersecurity threats.

An ensemble machine learning-based approach to predict thyroid disease using hybrid feature selection

Thyroid issues are becoming more and more common, and early detection is critical for therapy that reduces mortality and complications. Because of these factors, detecting thyroid problems has become more crucial in the medical field. Estimating the course of a disease accurately and understanding how clinical features interact are critical for medical diagnosis and treatment. All of these limits are overcome in our study by using a standard machine-learning model with proper clinical feature analysis and an ensemble-learning technique. Predicting sickness progression and the interdependence of clinical features or aspects are critical in medical diagnosis and therapy. However, machine learning has enabled us to detect the risk factors for this sickness. To select the best thyroid prediction outcome, we used five machine learning models in addition to the Ensemble ML classifier (hard voting). Class balancing approaches greatly increase classification performance. It has been demonstrated that using random oversampling improves classification results dramatically. Based on the experimental data, our suggested model outperforms existing methods by a wide margin. Using the XGBoost and SelectKBest feature selection strategies, the Ensemble ML classifier achieves the best results on hard voting on RF and DT, with 100 % sensitivity and 99.71 % accuracy. When features are decreased and the issue of high-class imbalance is addressed, the ensemble ML classifier (hard voting) performs better in dealing with classification challenges.

Data analytics in ensemble learning for effective crop yield prediction

In agricultural research, Crop Yield Prediction (CYP) offers the best decision-making to assist farmers in agricultural yield forecasting efficiently. Most of the existing studies have not considered the exhaustive exploration of data analytics techniques in Machine Learning (ML) for CYP due to which the existing models have not given the optimal results. The main objective of this study is to investigate the effectiveness of data analytics in Ensemble Learning (EL) techniques for more reliable and high-performance CYP models. This article proposes an expert system model, Blended Expert System for Crop Yield Prediction (BESCYP), designed to predict the precise crop yields for specific agricultural lands in the Assam state of India. The proposed BESCYP employs Expectation–Maximization (EM) algorithm to treat missing values, the Isolation Forest (IF) technique to analyze outliers, the Genetic Algorithm (GA) to perform feature selection, Robust Scaling (RS) technique to perform data normalization and the Extra Tree (ET) for classification that overcome the variance and overfitting problem commonly associated with standard ML algorithms. The evaluation of the proposed BESCYP model has been performed using accuracy, precision, recall, and F-1 score on a dataset obtained from International Crops Research Institute for Semi-Arid Tropics (ICRISAT). The proposed model is compared against different standard ML algorithms, EL algorithms and various existing models available in the literature, and the experimental results show that the proposed BESCYP model outperforms other models.

Comparative Analysis of Ensemble Learning Techniques for Purchase Prediction in Digital Promotion through Social Network Advertising

Comparative Analysis of Ensemble Learning Techniques for Purchase Prediction in Digital Promotion through Social Network Advertising

Environment Aspects and Daily Life-Threatening Risk Prediction for Improving Public Health Using Ensemble Learning Techniques

Increasing work nature in the fastest world need relaxing to maintain public health is important. But the continuous imbalance of work nature in human daily life doesn’t have time to consecrate the health with leads life threatening facts such as work tension, management stress, job professional, family needs, improper periodic work cycle, and soon. By analyzing the daily public life threatening risk prediction based on Feature selection and classification to category the public health to recommend for phycological treatment. By suggesting treatment schedule are important to balance the public health Lifecyle to make stress free life to spent with nature. Most of prevailing techniques analyse the life-threatening issues, but the features are improperly to taken without the mutual relation cause poor accuracy in precision and classifications rate with more false prediction rate. To tackle this issue, to propose an optimized ensemble learning Techniques based on multi scalar support vector machine (MSSVM) with deep scaled XGboost classifier (DSXG-boost) to improve the prediction accuracy. Initially the public life Cycle dataset is collected and to make normalization using Min-max normalization. Then the Public Life Threat Impact Rate (PLTIR) is analysed to marginalize the heath affecting features. Then then stress margin dependencies feature limits are selected with support of MSSVM. The selected features are grouped in cluster margins and classified with deep scaled XG boost algorithm to predict the active and inactive heath margin be categorized by risk threaten class. The proposed system prove the prediction accuracy in higher precision are by selecting mutual dependencies of health affection feature limits and best recall rate to improve the performance. Based on the predicted class, the categorized life threaten risk peoples are recommends to make phycological treatment to protect the public life cycle.