K-nearest Neighbor Research Articles

Gaze-Based Prediction of Students’ Math Difficulties - A Time Dynamic Machine Learning Approach to Enable Early Individual Assistance

Abstract Graphical differentiation substantially promotes the understanding of basic concepts of calculus. At the same time, such tasks are challenging for many students. To be able to support students in complex tasks during the solution process, it is important to recognise if students are having difficulties and, if so, to identify precisely what the difficulty is. However, previous research was only able to identify correct and incorrect solution processes but not the specific difficulties of students. In our study, the eye tracking data of 143 students reveal how students’ math tasks in context of graphically differentiation can be predicted. Retrospective-think-aloud (RTA) protocols were used to identify students’ difficulties and three machine learning algorithms, k-nearest neighbours (KNN), Random Forest and support vector machine (SVM), provide an accurate prediction for this multiclass problem. The prediction results improve with increasing processing time and achieve already good values long before the solution process of the tasks is completed. Our results show that certain student difficulties can be detected very early during the task solution process. Although this approach has been demonstrated for a sub-area of calculus, it is transferable to other fields of the STEM domain and therefore has much wider scope.

P0338 Early identification of pediatric inflammatory bowel disease based on machine learning with non-invasive data

Abstract Background Early identification of pediatric inflammatory bowel disease (IBD) can improve long-term prognosis, yet significant diagnostic delays persist. This study aims to develop an effective early identification tool using machine learning with non-invasive testing. Methods This retrospective study collected data from 314 pediatric patients (109 with IBD and 205 without IBD) hospitalized in Fudan University Children’s Hospital, to serve as the training dataset. We developed machine learning classifiers using support vector machine, artificial neural network, extreme gradient boosting, decision tree, random forest, k-nearest neighbors, logistic model, and gradient boosting machine methods, incorporating easily obtainable clinical examination tools. Participant data included age, sex, and three groups of features across different dimensions: IBD symptoms (diarrhea lasting ≥1 month; blood in stool for ≥1 week; recurrent perianal abscesses or fistulas; delayed growth; abdominal pain lasting ≥1 month; >10% weight loss; first-degree family history; arthritis, uveitis, erythema nodosum without definitive rheumatologic diagnosis; recurrent aphthous ulcers; unexplained fever), inflammatory biomarkers (elevated fecal calprotectin; elevated serum inflammatory markers; anemia; hypoalbuminemia; ANCA positivity), and transabdominal bowel ultrasound parameters (Limberg level >1 indicating bowel wall thickening with vascularity; mesenteric fat wrapping; bowel wall stratification disorder or loss; lymphadenopathy). We evaluated the performance of each feature group and their combinations, optimized hyperparameters, internal validation, and performed sequential validation with 66 prospectively recruited pediatric patients from the same center. The final model was selected based on optimal and stable performance in both the development and external validation cohorts, with Shapley values used to interpret feature importance. Results The support vector machine outperformed other algorithms, achieving an area under the curve of 0.95 in both internal and external validation datasets. The five most important features based on Shapley values were Limberg level >1, elevated ESR, elevated fecal calprotectin, recurrent perianal disease, and elevated CRP. Additionally, combining all three feature dimensions outperformed any single or paired combination. The probability of IBD classification showed moderate correlation with clinical, endoscopic, and histologic scores . Conclusion The machine learning model based on non-invasive data has the potential to become a low-cost and time-efficient tool for early identification of IBD. Ultrasound examination plays a significant role in distinguishing children with IBD from those without.

Machine Learning Models for Assistance from Soft Robotic Elbow Exoskeleton to Reduce Musculoskeletal Disorders

Musculoskeletal disorders are very common injuries among occupational and healthcare workers. These injuries are preventable in many scenarios using exoskeleton-based assistive technology. Soft robotics initiates an evolution in exoskeleton devices due to their safe human interactions, ergonomic design, and adaptive characteristics. Despite their enormous advantages, it is a challenging task to model and control soft robotic devices due to their inherent nonlinearity and hysteresis. Learning-based approaches are becoming more popular to overcome these limitations. This work proposes an approach to estimate the pressure input for a pneumatically actuated soft robotic elbow exoskeleton to assist occupational workers to avoid musculoskeletal disorders. An elbow exoskeleton design made up of modular pneumatic soft actuators is discussed, which helps to flex/extend an elbow joint. Machine learning (ML) approaches are used to develop a relationship between the air pressure, the bending angle of the elbow, and the percentage of the weight of the arm to be assisted by the exoskeleton. The most popular and widely used regression-based ML approaches are applied and compared in terms of accuracy and computation cost. Further, a modified KNN (K-Nearest Neighbor) approach is proposed, which outperforms the accuracy of other approaches.

Research on Coal and Gas Outburst Prediction and Sensitivity Analysis Based on an Interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine Model

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations.

A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries

Goal: The paper aims at the customization of supplier schedules upon the priority of key articles. It aims at the prediction of an appointment to a supplier based on the key aspects. The objective of this study is to investigate whether the machine learning algorithms can be used to predict the delivery dates of the products based on the trained data. Design/Methodology/Approach: The prediction method uses a machine learning approach. Prediction algorithms namely, Logistic Regression (LR), K-Nearest Neighbour (KNN), and Random Forest (RF) are used for forecasting. The appointment is assigned to a supplier based on the delivery date of a previous supplier order. If a supplier requests a prior date, then the average sales and opening stock of the products is verified and the prior date of n-3 will be assigned (‘n’ represents the assigned days of delivery). Results: Clustering is used to visualize the group of products based on the days of delivery and quantity ordered. This helps in creating the floor space for the on-time delivery of fast-moving products and reducing the manual process for the reordering team. The present work can be used for the procurement of key articles for the fulfillment of customer demands. The combination of K-Means with prediction and classification is giving lesser expected delivery date using RF compared to LR and KNN method. Limitations: The limitation of the current work is that it is applicable for small scale industries in the developing countries. Also deployment of the application in agricultural sectors allow for greater transparency and accountability in the supply chain. Practical Implications: Further, the work will also provide recommendations for retail companies looking to implement machine learning algorithms in their supply chain management. Originality/ Value: The case study developed a model for Retail industries to manage the supplier delivery appointments.

Detecting Malicious .NET Executables Using Extracted Methods Names

The .NET framework is widely used for software development, making it a target for a significant number of malware attacks by developing malicious executables. Previous studies on malware detection often relied on developing generic detection methods for Windows malware that were not tailored to the unique characteristics of .NET executables. As a result, there remains a significant knowledge gap regarding the development of effective detection methods tailored to .NET malware. This work introduces a novel framework for detecting malicious .NET executables using statically extracted method names. To address the lack of datasets focused exclusively on .NET malware, a new dataset consisting of both malicious and benign .NET executable features was created. Our approach involves decompiling .NET executables, parsing the resulting code, and extracting standard .NET method names. Subsequently, feature selection techniques were applied to filter out less relevant method names. The performance of six machine learning models—XGBoost, random forest, K-nearest neighbor (KNN), support vector machine (SVM), logistic regression, and naïve Bayes—was compared. The results indicate that XGBoost outperforms the other models, achieving an accuracy of 96.16% and an F1-score of 96.15%. The experimental results show that standard .NET method names are reliable features for detecting .NET malware.

Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.

The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin. Spectral data were preprocessed using multiple scattering correction (MSC), Savitzky-Golay smoothing (S-G), and standard normal variate (SNV). Content prediction models for paeoniflorin were developed using principal component regression (PCR), partial least squares regression (PLSR), and ridge regression (RR). Classification models for origin identification utilized support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). The SNV-RR model achieved a determination coefficient of 0.8943, while the SNV-SVM model achieved an accuracy of 0.9790. Meanwhile, two feature selection methods were used to further simplify the prediction model while ensuring accuracy, in order to improve the detection efficiency in practical applications. This research demonstrates the feasibility of combining NIR-HSI with machine learning for quality analysis of RPR, providing a theoretical basis for promoting hyperspectral imaging technology in the food and pharmaceutical sectors.

Cryptocurrency Market Volatility and Forecasting: A Comparative Analysis of Modern Machine Learning Models for Cryptocurrencies Predicting Accuracy

Cryptocurrency (CRP) has grown in popularity over the last decade. Since there is no central body to control the Bitcoin (BTC) markets, they are extremely volatile. However, several similar variables that cause price volatility in traditional markets also affect cryptocurrencies. Several bubble phases have taken place in BTC prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibited several bubble phases. Among traditional methods of analysis for this volatile market, only a small number of studies focused on Machine Learning (ML) techniques. The present study objective is to get an in-depth knowledge of the time series properties of CRP data and combine volatility models with ML models. In the hybrid method, we first apply the Nonlinear Generalized Autoregressive Conditional Heteroskedasticity (NGARCH) model with asymmetric distribution to calculate standardized returns, then forecast the UP and DOWN movement of standardized returns through ML models such as Logistic Regression (LR), Linear Discrimination Analysis (LDA), Quadratic Discrimination Analysis (QDA), Artificial Neural Networks (ANNs), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The findings show that the proposed hybrid approach of time series models and ML accurately predicts prices; specifically, the KNN model reveals that the scheme can be applicable to CRP market prediction. It is deduced that ML methods combined with volatility models have the tendency to better forecast this volatile market.

AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.

Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population. This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model. This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023. Data were collected using a mobile app (Gosufit) that measured depression, stress, anxiety, basal metabolic rate, oxygen saturation, heart rate, and average daily step count. Health coordinators recorded data on diabetes, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, percent body fat, and percent muscle. The presence of diabetes was the target variable, with various health indicators as predictors. Machine learning algorithms, including random forest, gradient boosting model, light gradient boosting model, extreme gradient boosting model, and k-nearest neighbors, were employed for analysis. The dataset was split into 70% training and 30% testing sets. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). Shapley additive explanations (SHAPs) were used for model interpretability. Significant predictors of diabetes included hypertension (χ²1=197.294; P<.001), hyperlipidemia (χ²1=47.671; P<.001), age (mean: diabetes group 72.66 years vs nondiabetes group 71.81 years), stress (mean: diabetes group 42.68 vs nondiabetes group 41.47; t3082=-2.858; P=.004), and heart rate (mean: diabetes group 75.05 beats/min vs nondiabetes group 73.14 beats/min; t3082=-7.948; P<.001). The extreme gradient boosting model (XGBM) demonstrated the best performance, with an accuracy of 84.88%, precision of 77.92%, recall of 66.91%, F1 score of 72.00, and AUC of 0.7957. The SHAP analysis of the top-performing XGBM revealed key predictors for diabetes: hypertension, age, percent body fat, heart rate, hyperlipidemia, basal metabolic rate, stress, and oxygen saturation. Hypertension strongly increased diabetes risk, while advanced age and elevated stress levels also showed significant associations. Hyperlipidemia and higher heart rates further heightened diabetes probability. These results highlight the importance and directional impact of specific features in predicting diabetes, providing valuable insights for risk stratification and targeted interventions. This study focused on modifiable risk factors, providing crucial data for establishing a system for the automated collection of health information and lifelog data from older adults using digital devices at service facilities.

Time series forecasting of bed occupancy in mental health facilities in India using machine learning

Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care and reduce the burden on healthcare facilities. This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. Accuracy of models were evaluated using Mean Absolute Percentage Error, and Diebold–Mariano test for assessing differences in predictive performance. Further, we forecast the bed occupancy, providing crucial insights for healthcare administrators in capacity planning and resource allocation, supporting data-driven decisions and enhancing the quality of mental health services in India.

Wood-waste classification using interpretable machine learning

ABSTRACT Determining and classifying the contents of wood wastes, which possess an extremely heterogeneous structure, is crucial for their optimal utilization. The heterogeneous nature of waste wood poses significant challenges for its separation and classification. Also, the classical chemometric methods are not ergonomic in terms of time and cost. For this reason, this study aimed to develop an efficient ML-based decision support system (DSS) for accurate classification of waste wood in addition to being an ergonomic approach versus chemometric methods. To develop ML-based DSS, firstly absorbance values at 650–4000 cm−1 wavelength were measured using FTIR-ATR device for 200 wood-waste samples. Absorbance values at 52 key wavelengths, identified as the most effective for characterizing the samples, were used as input features to estimate ML classification models using artificial neural networks (ANN), random forest (RF), ensemble learning, multiple logistic regression (MLR), discriminant analysis, Naive Bayes, K-nearest neighbour (KNN), and support vector machines (SVM). The analysis results indicated that the developed DSS achieved quite a high classification accuracy rates, approaching 100%. As a result, this kind of ML-based DSS can be used as a practical and efficient tool to determine and classify the contents of wood wastes.

A Spiking Neural Network Approach for Classifying Hand Movement and Relaxation from EEG Signal using Time Domain Features

High-performance prosthetic and exoskeleton systems based on EEG signals can improve the quality of life of hand-impaired people. Effective controlling of these assistive devices requires accurate EEG signal classification. Although there have been advancements in the assistive Brain-Computer Interface (BCI) systems, still classifying the EEG signals with high accuracy is a great challenge. The objective of this research is to investigate the accuracy of the EEG signal classification of the Spiking Neural Network (SNN) classifier for factual and exact control of prosthetic and exoskeleton systems for individuals with hand impairment. The EEG dataset has been taken from the BNCI Horizon 2020 website, which is for hand movement-relax events of a patient with high spinal cord injury (SCI) to operate a neuro-prosthetic device attached to the paralyzed right upper limb. The fusion of Dispersion Entropy (DE), Fuzzy Entropy (FE), and Fluctuation based Dispersion Entropy (FDE) with mean and skewness features are extracted from the Motor Imagery (MI) EEG signals and applied to the Spiking Neural Network (SNN) classifier. To compare the performance of this algorithm, these same features have been used in Convolutional Neural Network (CNN), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) classifiers. It has been found that SNN has given the highest classification accuracy of 80% with a precision of 80.95%, recall of 77.28%, and F1-score of 79.07%. This indicates that SNN with these five features has greater potential in BCI system-based applications.

Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids.

Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)-to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three chemometric approaches-random forest, support vector machine, and k-nearest neighbor algorithms-were employed and optimized using a 5-fold cross-validation grid search for all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level). The enhanced performance of the high-level fusion approach (F1 score of 92%) is demonstrated, effectively leveraging the surface-enhanced Raman data with a 90% voting weight, without compromising prediction accuracy (92%) when combined with infrared spectral data. This highlights the viability of a multi-instrument approach using data fusion and random forest classification to improve the detection of various components in complex opioid samples in a point-of-care setting.

Performance of K-Nearest Neighbor Algorithm and C4.5 Algorithm in Classifying Citizens Eligible to Receive Direct Cash Assistance in Bandar Mahligai Village

Direct Cash Assistance, commonly called BLT, is one of the many programs the Indonesian government held to reduce the poverty rate of the Indonesian population. This study compares the KNN and C4.5 methods to determine the eligibility of residents eligible to receive Direct Cash Assistance in Bandar Mahligai Village. This study began with collecting resident data from the Bandar Mahligai village office. Then, the data obtained was taken into several attributes to be used in the classification process, namely the name of the head of the family, KK number, NIK, number of dependents, occupation, income, and monthly expenses. After the data is collected, the data will be classified using the KNN and C4.5 algorithms. There is a significant difference between the two algorithms in the classification process; the KNN algorithm by looking for the nearest neighbor data value, in this study, the K value = 9, while the C4.5 algorithm by building a decision tree from the attribute values taken based on resident data used as training data. The classification results of the two methods will be compared using a confusion matrix to obtain a higher accuracy technique. The results of testing using a confusion matrix for both algorithms are the accuracy produced by the KNN and C4.5 algorithms in classifying residents eligible for Direct Cash Assistance (BLT) of 90% in the system that has been built. The results of comparing the KNN and C4.5 algorithms for this study show that the KNN algorithm is better because the accuracy level reaches 90% in manual and system calculations. While the C4.5 method only gets 85% for the accuracy of its manual calculations, it receives an accuracy level of 90% in the system that has been built.

MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental methods, accurate computational approaches for protein-protein interaction sites (PPIS) prediction can save significant time and costs. In this study, we propose a novel model named MVGNN-PPIS. To the best of our knowledge, it is the first to utilize predicted structures generated by AlphaFold3, and combined with transfer learning techniques, for predicting PPIS. This approach addresses the limitations of traditional methods that depend on native protein structures and multiple sequence alignments (MSA). Additionally, we introduced a multi-view graph framework based on two types of graph structures: the k-nearest neighbor graph and the adjacency matrix. By alternately employing a Graph Transformer and Graph Convolutional Networks (GCN) to aggregate node information, this framework effectively captures both local and global dependencies of each residue in the predicted structures, thereby significantly enhancing the model's sensitivity to binding sites. This framework further integrates direction, distances and angular information between the 3D coordinates of side-chain atom centroids to construct a relative coordinate system, generating enhanced edge features that ensure the model's equivariance to molecular translations and rotations in space. During training, the Focal Loss function is employed to effectively address the class imbalance in the dataset. Experimental results demonstrate that MVGNN outperforms the current state-of-the-art methods across multiple PPIS benchmark datasets. To further validate the model's generalization capability, we extended MVGNN to the domain of predicting protein-nucleic acid interaction sites, where it also achieved superior performance.

K-Nearest Neighbour Estimation of the Conditional Set-Indexed Empirical Process for Functional Data: Asymptotic Properties

k-Nearest Neighbour Estimation of the Conditional Set-Indexed Empirical Process for Functional Data: Asymptotic Properties

Robust fault detection and classification in power transmission lines via ensemble machine learning models

Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. An ensemble methodology, RF-LSTM Tuned KNN, is proposed to enhance detection accuracy and robustness. Results indicate that RF-LSTM Tuned KNN achieves a remarkable accuracy of 99.96% on a multi-label dataset, outperforming RF (97.50%) and KNN (96.55%). In binary classification, KNN attains the highest accuracy of 99.85%, closely followed by RF at 99.72%. This methodology provides significant advancements in fault detection capabilities, offering valuable insights for improving grid reliability and stability, and ensuring a more resilient power supply.

Machine learning models for easily obtainable descriptors of the electrocatalytic properties of Ag-Pd-Ir nanoalloys toward the formate oxidation reaction.

Direct formate fuel cells (DFFCs) have received increasing attention due to their environmentally benign and highly safe characteristics. However, the absence of highly active electrocatalysts for the formate oxidation reaction (FOR) restricts their widespread application. Currently, the design of FOR catalysts, which relies on experimental trial-and-error and high-throughput DFT calculations, is costly and time-consuming. In this study, based on a DFT dataset of FOR overpotentials for 137 Ag-Pd-Ir nanoalloy catalysts, six machine learning (ML) models were trained, where the K-nearest neighbors (KNN) model demonstrated the best performance, with an R2 value of 0.94, an MAE value of 0.041 V and an RMSE value of 0.050 V. Using the KNN model, six optimal catalysts with an overpotential of 0.48 V were screened from 310 candidate catalysts, with an MAE value as low as 0.004 V compared to the DFT results, proving the accuracy of the ML model. This work provides a novel strategy to accelerate the design of high-performance catalysts.

Kidney Disease Prediction

Chronic Kidney Disease (CKD) is a growing public health concern, and early detection is critical for effective intervention. This study compares various machine learning models—Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks—to predict CKD. Results show that ensemble methods, like Random Forests, outperform individual models in terms of accuracy and robustness due to their ability to reduce overfitting. However, simpler models like Logistic Regression and Decision Trees offer greater interpretability, which is essential in clinical settings. The study underscores the trade-off between model complexity and transparency, suggesting future research should focus on integrating predictive models with Electronic Health Records (EHRs) to facilitate real-time monitoring and improve patient care. KeyWords: Chronic Kidney Disease, Machine Learning, Random Forest, Logistic Regression, K-Nearest Neighbors, Early Detection.

Using Machine Learning for Aerostructure Surface Damage Digital Reconstruction

Aerostructure surface damage inspection is carried out over the whole life-cycle using legacy processes and recording during maintenance. The inspection techniques record the detailed history of the damage and repair. However it remains elusive to predict the location of future damage on the aerostructure surface. In this paper, we work up a novel simulation technique based on the results of machine learning analysis for prediction of the reference location of future aerostructure surface damage. First, we use the support vector machine (SVM) and k-nearest neighbor (KNN) to analyze the damage on three B777-200 aircraft and find that the classification accuracy can range from 75.1% to 86%. Then, we use the prediction result of a feedforward neural network (FNN) to simulate the damage structure and the mapping relationship to explore its reconstructive possibility. We show that the aerostructure surface damage can be reconstructed by machine learning. Moreover, the aerostructure surface damage heat map obtained by reconstruction can be prepared for further image recognition analysis in the future.