Machine Learning Models For Prediction Research Articles

Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.

Optimization of Crop Yield Prediction Through Linear Modelling and Deep Learning Techniques Used in Precision Agriculture

The substantial advancements in computer science and engineering have sparked interest in precision agriculture and led to the development of more advanced instruments and methods for enhancing farming practices. This study focuses on the use of machine learning and mathematical models for fertilisation optimisation and yield prediction as part of a Precision Agriculture strategy. In particular, provides the outcomes of forecasting winter wheat production and protein content across four farms using the amounts of nitrogen fertiliser sprayed on the fields. To maximise net yields on the next crop, fertiliser treatments have to be prescribed based on these projections. In particular, contrast approaches based on neural networks (deep and shallow) and multiple regression (linear and non-linear). The greatest results are obtained by a deep neural network that incorporates spatial sampling and is based on the stacked autoencoder, according to the findings.

Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey

Loss to follow-up (LTFU) in tuberculosis (TB) management increases morbidity and mortality, challenging effective control strategies. This study aims to develop and evaluate machine learning models to predict loss to follow-up in TB patients, improving treatment adherence and outcomes. Retrospective data encompassing tuberculosis patients who underwent treatment or registration at the National Center for Clinical Medical Research on Infectious Diseases from January 2017 to December 2021 were compiled. Employing machine learning techniques, namely SVM, RF, XGBoost, and logistic regression, the study aimed to prognosticate LTFU. A comprehensive cohort of 24,265 tuberculosis patients underwent scrutiny, revealing a LTFU prevalence of 12.51% (n = 3036). Education level, history of hospitalization, alcohol consumption, outpatient admission, and prior tuberculosis history emerged as precursors for pre-treatment LTFU. Employment status, outpatient admission, presence of chronic hepatitis/cirrhosis, drug adverse reactions, alternative contact availability, and health insurance coverage exerted substantial influence on treatment-phase LTFU. XGBoost consistently surpassed alternative models, boasting superior discriminative ability with an average AUC of 0.921 for pre-treatment LTFU and 0.825 for in-treatment LTFU. Our study demonstrates that the XGBoost model provides superior predictive performance in identifying LTFU risk among tuberculosis patients. The identification of key risk factors highlights the importance of targeted interventions, which could lead to significant improvements in treatment adherence and patient outcomes.

Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy.

In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine learning (ML) models to identify key features associated with ED and predict its occurrence in children undergoing tonsillectomy or adenotonsillectomy. The analysis involved data cleaning, exploratory data analysis (EDA), supervised predictive modeling, and unsupervised learning on a medical dataset (n = 423). After preliminary data cleaning, EDA encompassed plotting histograms, boxplots, pairplots, and correlation heatmaps to understand variable distributions and relationships. Four predictive models were trained including logistic regression (LR), random forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGBoost). The models were evaluated and compared using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), precision, recall, and feature importance. The RF model showed better performance and was used for the test (AUC-ROC 0.96, precision 1.00, and recall 0.92 on the validation set). K-means clustering was applied to find groups within the data. Elbow method and silhouette scores were used to determine the optimal number of clusters. The formed clusters were analyzed by aggregating features to understand the characteristics of each cluster. EDA revealed significant positive correlations between age, weight, American Society of Anesthesiologists (ASA) health score, and surgery duration with the risk of developing ED. Among the ML models, RF achieved the highest performance. Key predictive variables, based on the model's feature importance, included delirium screening scales, extubation time, and time to regain consciousness. Unsupervised K-means clustering identified 2-3 optimal clusters, which represented distinct patient subgroups: younger, healthier, low-risk individuals (cluster 0), and older patients with increasing chronic disease burden, higher delirium screening scores, and consequently higher post-operative delirium risk (clusters 1 and 2). ML techniques are valuable tools for extracting insights and making accurate predictions from healthcare data. High-performing algorithm-based models can be implemented for clinical decision support systems, facilitating early identification and intervention for ED in pediatric patients. By investigating various variables, it is possible to assess risk and implement preventive measures effectively. Furthermore, unsupervised clustering reveals distinct patient subgroups, enabling personalized perioperative management strategies and enhancing overall patient care.

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach.

Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM). Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values. Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4months (interquartile range [IQR] 29.9-46.7) versus 120.5months (IQR 97.2-134.0), respectively (p<0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/. An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

The complexities of permafrost changes, driven by climate warming and engineering activities, coupled with challenges in data acquisition, make it crucial and challenging to accurately predict the artificial permafrost table, particularly for subgrades in high-temperature unstable permafrost regions. To address this, this study developed a hybrid machine learning model (RF-LSTM-XGBoost) for permafrost table prediction. By analyzing climate change and ground temperature data from various positions and depths along the subgrade in the Tuotuo River section of the Qinghai-Xizang Highway, the Spearman correlation coefficient method was initially used to determine the important influencing factors. Random Forest (RF), Long Short-Term Memory Neural Network (LSTM), and Extreme Gradient Boosting (XGBoost) models were used to predict the artificial permafrost table, and grid search and cross-validation methods were employed to optimize the hyperparameters of each model. A linear weighted combination method based on the minimum cumulative absolute error was utilized to merge the models, and its performance was compared with the individual RF, LSTM, and XGBoost models. Subsequently, the feature importance of the variables in the machine learning model was analyzed. The results indicated a strong correlation between artificial permafrost table changes and factors such as daily average atmospheric temperature, subgrade surface ground temperature, and subgrade surface ground heat flux during the freezing-thawing cycle. The combined model highlighted daily atmospheric temperature as the most influential predictor, followed by ground heat flux, with the surface ground temperature being less impactful. The combined model demonstrated improved predictive accuracy, with MSE, MAPE, RMSE, MAE, and R2 values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, surpassing those of individual models. This model offers a rapid, accurate, and reliable approach for permafrost table prediction, advancing subgrade stability research in challenging permafrost environments.

Developing A Machine Learning Predictive Model For Learning Analytics In Higher Education

Developing A Machine Learning Predictive Model For Learning Analytics In Higher Education

Machine Learning Diagnostic Model for Early Stage NSTEMI: Using hs-cTnI 1/2h Changes and Multiple Cardiovascular Biomarkers.

This study demonstrates differences in the distribution of multiple cardiovascular biomarkers between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) patients. Diagnostic machine learning predictive models measured at the time of admission and 1/2 h post-admission, achieving competitive diagnostic predictive results. This study aims to explore the diagnostic value of changes in high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with suspected NSTEMI. A total of 267 patients presented with chest pain, requiring confirmation of acute coronary syndrome (ACS) subtypes (NSTEMI vs. UA). Hs-cTnI and other cardiac markers, such as creatine kinase-MB (CK-MB) and Myoglobin (Myo), were analyzed. Machine learning techniques were employed to assess the application of hs-cTnI level changes in the clinical diagnosis of NSTEMI. Levels of CK-MB, Myo, hs-cTnI measured at admission, hs-cTnI measured 1-2 h after admission, and NT-proBNP in NSTEMI patients were significantly higher than those in UA patients (p < 0.001). There was a positive correlation between hs-cTnI and CK-MB, as well as Myo (R = 0.72, R = 0.51, R = 0.60). The optimal diagnostic model, Hybiome_1/2h, demonstrated an F1-Score of 0.74, an AUROC of 0.96, and an AP of 0.89. This study confirms the significant value of hs-cTnI as a sensitive marker of myocardial injury in the diagnosis of NSTEMI. Continuous monitoring of hs-cTnI levels enhances the accuracy of distinguishing NSTEMI from UA. The models indicate that the Hybiome hs-cTnI assays perform comparably well to the Beckman assays in predicting NSTEMI. Moreover, incorporating hs-cTnI measurements taken 1-2 h post-admission significantly enhances the model's effectiveness.

Introducing Spatial Heterogeneity via Regionaliza-tion Methods in Machine Learning Models for Geo-graphical Prediction: A Spatially Conscious Para-digm

Introducing Spatial Heterogeneity via Regionaliza-tion Methods in Machine Learning Models for Geo-graphical Prediction: A Spatially Conscious Para-digm

Artificial intelligence for predicting arctic permafrost and active layer temperatures along the Alaskan North Slope

AbstractClimate change in Arctic regions poses a threat to permafrost stability, potentially leading to issues such as infrastructure damage, altered ecosystems, and increased greenhouse gas emissions. The challenge of monitoring permafrost temperatures and identifying critical environmental factors is accentuated by the scarcity of subsurface thermal data in remote Arctic locales. To alleviate this lack of soil thermal data we set out to combine in situ observed soil temperatures with MERRA-2 reanalysis data and Machine Learning (ML) to develop local and season-specific subsurface soil temperature predictions in Alaska. First, reanalysis features are selected based on surface energy budget physics. Coupled with Julian calendar dates, reanalysis features are preprocessed and then fed to the ML prediction model. We conducted a series of computational experiments and tested the results against performance benchmarks to determine the optimal prediction models, length of look-back period of model inputs, and the training set size. Six conventional ML models (e.g., GBDT, RF, SVR) and five statistical baselines (e.g. ARIMA) using in situ time series of soil temperature data ranging from 0 - 1 m depth are considered for the prediction task. The models are trained using in situ soil temperatures at depths between 0 and 1 m, spanning 16+ years, from field sites at Deadhorse and Toolik Lake, Alaska. All prediction performances are assessed using root mean squared error (RMSE) and mean absolute error (MAE). Results show that locally trained ML models can estimate shallow soil temperatures with an average error of $$RMSE=1.308^{\circ }$$ R M S E = 1 . 308 ∘ C.

Interpretable machine‐learning models for predicting creep recovery of concrete

AbstractCreep recovery of concrete is essential for accurately assessing the performance of concrete structures over service time. Existing creep recovery models exhibit low accuracy, and the influencing factors of creep recovery remain inadequately elucidated. In this paper, interpretable machine learning (ML) techniques were employed to develop a prediction model for concrete creep recovery. Several ML techniques were selected including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM). In order to maximize the sample size of the dataset, 109 sets of creep recovery data were collected from existing literatures for model training. Feature selection is utilized to determine the input parameters for ML models, and 12 input variables were selected. The model is fine‐tuned using Bayesian optimization techniques. To ensure the reliability of ML models, 10‐fold cross‐validation and random data splitting were implemented. The results indicate that the ML models exhibited higher accuracy compared to the existing creep recovery model. Among these ML models, LGBM demonstrated superior accuracy, efficiency and stability (with R2 = 0.993, 0.978, and 0.973 for the training, testing, and validation sets, respectively). Shapley additive explanations (SHAP) were employed to interpret the significance of each input parameter on ML model prediction. Duration after unloading, stress magnitude, and ambient relative humidity were the main feature variables influencing concrete creep recovery. Upon comparing the influencing factors, it was discerned that there exists a distinct difference between creep and creep recovery of concrete.

Comparison of deep and conventional machine learning models for prediction of one supply chain management distribution cost

Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it’s essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources.

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Comparative Analysis of Machine Learning Models for Accurate Flight Price Prediction

Flight fare prediction is a vital component in helping consumers make informed decisions regarding travel expenses. Airline ticket prices fluctuate due to a variety of factors such as demand, time of purchase, and flight routes. In this research, we propose a machine learning-based solution for predicting flight fares using historical data. Models like Random Forest, Gradient Boosting, and Support Vector Machines (SVM) are employed to analyze flight data and produce reliable predictions. This study demonstrates how predictive models can benefit customers by offering insights into pricing trends, thus optimizing their flight booking process.

Machine Learning-Based Strength Prediction of Round-Ended Concrete-Filled Steel Tube

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design.

Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil

Soil erosion is a global issue—with gully erosion recognized as one of the most important forms of land degradation. The purpose of this study is to compare and contrast the outcomes of four machine learning models, Classification and Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), used for mapping susceptibility to soil gully erosion. The controlling factors of gully erosion in the Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation in Google Earth and gully erosion samples (n = 159) were used for modelling and spatial prediction. The XGBoost and RF models achieved identical results for the area under the receiver operating characteristic curve (AUROC = 88.50%), followed by the SVM and CART models, respectively (AUROC = 86.17%; AUROC = 85.11%). In all models analysed, the importance of the main controlling factors predominated among Lineaments, Land Use and Cover, Slope, Elevation and Rainfall, highlighting the need to understand the landscape. The XGBoost model, considering a smaller number of false negatives in spatial prediction, was considered the most appropriate, compared to the Random Forest model. It is noteworthy that the XGBoost model made it possible to validate the hypothesis of the study area, for susceptibility to gully erosion and identifying that 9.47% of the Piraí Drainage Basin is susceptible to gully erosion. Furthermore, replicable methodologies are evidenced by their rapid applicability at different scales.

Degradation law of bond strength of reinforced concrete with corrosion-induced cracks and machine learning prediction model

During long term operation service, reinforced concrete (RC) structures are subjected to corrosive media such as chloride salts, which results in the degradation of interface bond performance, subsequently reducing the load bearing capacity and service life of the structures. To accurately assess the durability of RC structures in environments such as oceans and salt lakes, this study combines experimental methods with machine learning (ML) techniques to explore the degradation patterns and predictive models of bond performance under chloride salt corrosion. Accordingly, a series of central pull-out tests were conducted based on an electrochemical accelerated-dry-wet cycle corrosion regime to investigate the degradation patterns of bond performance between corroded steel bars and concrete under different corrosion degree and corrosion-induced crack widths. By comparatively analyzing the evolution patterns of cracks and the morphology of the steel bars in reinforced concrete specimens and their failure modes, and integrating the mechanical degradation mechanism with chemical and microscopic corrosion mechanisms, an in-depth analysis of the corrosion-induced degradation patterns at the bond interface was performed. The internal relationships between corrosion degree, corrosion-induced crack width and bond strength were discussed, and the variation patterns of bond strength with corrosion-induced crack width and corrosion degree were explored. Moreover, the bond strength patterns were analyzed from an energy perspective. The results indicate that bond strength and bond energy decrease with an increase in the corrosion-induced crack width of the concrete. Additionally, based on the experimental data from this study, ML predictive models for bond strength were established using corrosion-induced crack width as the control variable. The results show that the ML-based bond strength predictive models fit well with the experimental data, offering higher accuracy and reliability compared to traditional bond strength models. The research findings provide significant theoretical basis and practical guidance for safety assessment, maintenance, reinforcement, and full-life design of corroded RC structures.

Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm

Chloride ions infiltrate concrete structures, causing corrosion of the steel reinforcement, which creates concrete spalling and decreases load-bearing capacity, ultimately leading to structural failure. Thus, it is essential to comprehend the diffusion process of chloride ions in concrete. Current models used to predict the chloride ion diffusion in concrete are hindered by poor accuracy due to their simplistic consideration of variables and the complex interplay of environmental and material factors affecting key parameters such as surface chloride concentration (Cs) and the chloride diffusion coefficient (D). These influences are challenging to capture with simple linear or non-linear relationships, and traditional machine learning models often overfit training data and underperform with new data. To address these challenges, this study presents physical model experiments conducted to explore the diffusion process of chloride ions under multi-field coupling conditions, examining chloride ion concentrations across different concrete layers under varying environmental temperatures (T), humidities (h), erosion times (t), water-cement ratios (W/C), and volumes of coarse aggregates (v). This study reveals the distribution patterns of chloride ions within concrete layers. An XGBoost machine learning predictive model was developed using environmental temperature, humidity, erosion time, water-cement ratio, and coarse aggregate volume as input variables, with Cs and D as output variables. The results show that when the water-cement ratio reaches 0.5 in high humidity, the SHAP value rises to 0.015, enhancing chloride diffusion. As erosion time exceeds 200 days and temperature surpasses 38 °C, the SHAP value peaks at 0.02. Additionally, when coarse aggregate volume is between 0.45 and 0.60, temperature changes have little effect on Cs. Additionally, a graphical user interface was developed for modeling Cs and D to enhance practical usability.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations.