Feature Selection Techniques Research Articles

A protein fitness predictive framework based on feature combination and intelligent searching.

Machine learning (ML) constructs predictive models by understanding the relationship between protein sequences and their functions, enabling efficient identification of protein sequences with high fitness values without falling into local optima, like directional evolution. However, how to extract the most pertinent functional feature information from a limited number of protein sequences is vital for optimizing the performance of ML models. Here, we propose scut_ProFP (Protein Fitness Predictor), a predictive framework that integrates feature combination and feature selection techniques. Feature combination offers comprehensive sequence information, while feature selection searches for the most beneficial features to enhance model performance, enabling accurate sequence-to-function mapping. Compared to similar frameworks, scut_ProFP demonstrates superior performance and is also competitive with more complex deep learning models-ECNet, EVmutation, and UniRep. In addition, scut_ProFP enables generalization from low-order mutants to high-order mutants. Finally, we utilized scut_ProFP to simulate the engineering of the fluorescent protein CreiLOV and highly enriched mutants with high fluorescence based on only a small number of low-fluorescence mutants. Essentially, the developed method is advantageous for ML in protein engineering, providing an effective approach to data-driven protein engineering. The code and datasets for scut_ProFP are available at https://github.com/Zhang66-star/scut_ProFP.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot. In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets. In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated. 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy. The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.

A detection model of aggressive driving behavior based on hybrid deep learning

<p>Modern transportation faces a crucial challenge in ensuring road safety by addressing driving behavior concerns. This paper introduces an innovative deep learning model derived from a cellphone-collected Driving Behavior dataset, focusing on detecting and classifying aggressive driving. Using a cohort-based dataset, a hyper-deep learning model categorizes drivers into normal, slow, and aggressive groups. The system employs pre-processing methods and two methodologies, directly inputting data and incorporating feature selection. The hyper-CNN-Dense model, used for training, shows promising results. Feature selection techniques like SVD6 and MI6 achieve optimal outcomes, with a 100% accuracy rate in detecting aggressive driving. Notably, SVD6 boasts a short processing time of just 43 seconds. This research successfully identifies aggressive driving behavior with impeccable accuracy and in a remarkably short timeframe.</p>

Hybrid similarity based feature selection and Cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person’s comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Gesture recognition framework for upper-limb prosthetics using entropy features from electromyographic signals and a Gaussian kernel SVM classifier

This paper puts forward a novel entropy features based multi-class SVM classifier framework to predict the limb movement of the transradial amputees from the surface electromyography (sEMG) signals. The major challenges with the sEMG signal are nonlinear and non-stationary characteristics and susceptibility to noise. Consequently, a robust and an effective feature extraction framework which is invariant to force level variations is central in sEMG based prosthesis control. To address the aforementioned challenges, this study leverages the potential of variational mode decomposition (VMD) technique to identify the prominent frequency modes of the sEMG signals, and performs the spectral evaluation of the decomposed sEMG modes to identify the dominant ones to extract the entropy features. Subsequently, we evaluate the efficacy of four nonlinear optimal feature selection techniques and identify the prominent entropy features to train the multi-class SVM model that can predict the gestures. Specifically, to handle the nonlinearly separable input data, this study implements a kernelization named a radial basis function (RBF), which has good generalization and noise tolerance features. The efficacy of the proposed framework is tested using the publicly available datasets that contain gestures from transradial and congenital amputees for functional gestures. Experimental results obtained for various gestures with dynamic force levels underscore that the proposed framework is highly robust against the force level variations and can achieve a classification accuracy of 99.07%.

Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.

Comparative Analysis of ML Models with Selection Methods for Early Predictive Analytics of Sepsis in ICU

Objectives: This study aims to make early prediction of Sepsis using ML algorithms and provide comparative analysis of different feature selection techniques. Methods: In this study, the Physionet website dataset has been used. Sepsis categorization is done on the basis of Sepsis-3 criteria. The dataset used is highly imbalanced, with only 2% of the data belonging to Sepsis. The SmoteTomek technique is used to handle an imbalanced dataset. Various filter, embedded, and wrapper feature selection techniques, like tree-based feature selection technique, recursive feature elimination (RFE), information gain, Bhattacharya distance, lasso, etc., have been applied for the top-performing classification ML models. These selection techniques were applied to RandomForest, K Nearest Neighbors (KNN), and Decision Tree models. We compared the impact of these selection techniques on the aforesaid machine learning models. Findings: After applying the RFE technique, the Area Under the Receiver Operating Characteristic curve (AUROC) score of the RandomForest model has slightly increased from 0.996 to 0.9974. KNN model with a tree-based feature selection technique showed the highest sensitivity of 0.934. which is slightly higher than the sensitivity of 0.922, which was without applying any feature selection technique. Along with the AUROC score, the highest performance, in terms of specificity (0.9976), accuracy (0.9959), and f-measure score (0.9062), is achieved when RFE is applied to the RandomForest model. The best selection algorithm for decision trees and KNN is the tree-based selection technique. RFE is the best selection technique for RandomForest. Novelty: In this research, the AUROC score is slightly increased to 0.9974, which has not been achieved yet. Instead of 40, the number of features chosen is 20. This research also provides a comparison of different feature selection techniques like tree-based feature selection, information gain, Bhattacharya, and RFE. It also analyses their impact on the performance of models, which has not been done yet with the same set of selection techniques. Keywords: Sepsis, Machine Learning, Feature selection, Early prediction, Predictive Analytics

Perovskite Solar Cell Stability Analysis Using Entropy‐Based Support Vector Machines Learning

ABSTRACTLead halide perovskites have demonstrated significant potential for photovoltaic (PV) applications over the past 10 years. Perovskite solar cells (PSCs) stability, however, continues to limit their commercialization, and the inability to compare previous stability data to assess possible directions for increasing device stability is caused by a lack of effectively established unified stability testing and disseminating standards. In this article, we suggest applying machine learning (ML) to improve the thermal, chemical, and structural stability of PSCs. Data normalization and data augmentation are common preprocessing steps that are where the process starts. Then, using the Modified Grasshopper Optimisation Algorithm (MGO), feature selection techniques are used to remove unnecessary or irrelevant features. Finally, there is a novel machine learning technique that uses support vector machines (ESVM) that are based on entropy to forecast the stability classification of stable/unstable. The proposed reaches an accuracy of 0.99% far better than the proposed methods.

Impact of Feature Selection Techniques on the Performance of Machine Learning Models for Depression Detection Using EEG Data

Major depressive disorder (MDD) poses a significant challenge in mental healthcare due to difficulties in accurate diagnosis and timely identification. This study explores the potential of machine learning models trained on EEG-based features for depression detection. Six models and six feature selection techniques were compared, highlighting the crucial role of feature selection in enhancing classifier performance. This study investigates the six feature selection methods: Elastic Net, Mutual Information (MI), Chi-Square, Forward Feature Selection with Stochastic Gradient Descent (FFS-SGD), Support Vector Machine-based Recursive Feature Elimination (SVM-RFE), and Minimal-Redundancy-Maximal-Relevance (mRMR). These methods were combined with six diverse classifiers: Logistic Regression, Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). The results demonstrate the substantial impact of feature selection on model performance. SVM-RFE with SVM achieved the highest accuracy (93.54%) and F1 score (95.29%), followed by Logistic Regression with an accuracy of 92.86% and F1 score of 94.84%. Elastic Net also delivered strong results, with SVM and Logistic Regression both achieving 90.47% accuracy. Other feature selection methods yielded lower performance, emphasizing the importance of selecting appropriate feature selection and machine learning algorithms. These findings suggest that careful selection and application of feature selection techniques can significantly enhance the accuracy of EEG-based depression detection.

Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions

AbstractWireless sensor network (WSN) cluster‐based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high‐level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster‐based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co‐authorship, co‐occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

Robust remote detection of depressive tendency based on keystroke dynamics and behavioural characteristics.

Depressive Disorder (DD) is a leading cause of disability worldwide. Screening tools for detecting DD symptoms are essential for monitoring and efficient managing. Remarkably, individuals' kinetic activities, including their interaction with touchscreen smartphones, can be a proxy for their mental status. Therefore, studying these typing patterns can assist in developing passive screening tools for detecting even the early stage of DD, i.e., the depressive tendency (DT). Here we extend a previous study by exploring different machine learning models with various feature engineering approaches to detect the subjects' DT, as indicated by the self-administered Patient Health Questionnaire-9 (PHQ-9) score, via keystroke digital biomarkers. The keystroke timing sequences were unobtrusively collected from 24 subjects during routine interaction with touchscreen smartphones, resulting in 23,264 typing sessions. The proposed framework was investigated under two keystroke feature combinations-hold-time and flight-time variables-and validated using nested cross-validation scheme. Different feature selection (FS) techniques were employed to select informative features from the keystroke sequences. The best-performing gradient boosting classifier with features selected by the mutual information FS method achieved an improved Area Under Curve (AUC) of 0.98 [95% confidence interval: 0.91-1.00]. The proposed DT pipeline, which surpasses the state-of-the-art models, could effectively capture DT, considering users' behavioural characteristics. This would potentially provide users with information regarding the evolution of their mental health, simultaneously contributing to improving digital tools for objectively screening mental disorders in-the-wild.

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.

To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU). Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level. Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages. The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings.

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. This study presents a robust methodology for classifying leaf images within the Cope Leaf Dataset by enhancing the feature extraction and selection process. Cope Leaf Dataset has 99 classes and 64 features with 1584 records. Features are extracted based on the margin, texture, and shape of the leaves. It is challenging to classify a large number of labels because of class imbalance, feature complexity, overfitting, and label noise. Our approach combines advanced feature selection techniques with robust preprocessing methods, including normalization, imputation, and noise reduction. By systematically integrating these techniques, we aim to reduce dimensionality, eliminate irrelevant or redundant features, and improve data quality. Increasing accuracy in classification, especially when dealing with large datasets and many classes, involves a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. The results indicate that the Multilayer Perception algorithm gives 89.48%, the Naïve Bayes Classifier gives 89.63%, Convolutional Neural Networks has 88.72%, and the Hoeffding Tree algorithm gives 89.92% accuracy for the classification of 99 label plant leaf classification problems.

HPB O06 - A computer tomography-based radiomics signature predicts overall survival following curative resection of colorectal liver metastasis: A multicentre, retrospective study

Abstract Background Colorectal cancer liver metastasis (CRLM) has a heterogeneous outcome and improved prognostic biomarkers are needed to aid patient-tailored management. Here, we assess the value of Computer Tomography (CT)-guided radiomics in the prognostication of post-resection CRLM using data from two large tertiary referral centres in the UK. Method Patient data was extracted from prospectively maintained databases of patients undergoing CRLM resection at two centres. CRLM and regions of interest from background normal liver were segmented. Radiomics features were extracted from portal venous phase CT images using open-source software. Feature selection techniques were applied. Machine learning models incorporating radiomic or clinical features, and their combination, were developed and assessed for mortality prediction at fixed time points. Cox proportional hazard regression was utilised for risk score generation to classify patients as high or low-risk for overall survival estimation. The models were evaluated on unseen testing data. Results Radiomics features (269) were generated from 959 metastases in 399 patients across the two sites. Models with combined clinical and radiomic features performed similarly to those with radiomic features alone, and often outperformed models with clinical features alone demonstrating good discrimination and risk prediction at 2 and 3 years. The computed radiomic scores allowed us to generate Kaplan–Meier curves which showed CRLM patients in the high-risk group had a lower overall survival vs the low-risk group(p<0.05). This risk-score independently predicted mortality in our multivariate Cox proportional-hazards model. Conclusion We demonstrate a robust combination of CT-based radiomics features can predict CRLM outcomes across two different hospital sites. Our radiomic risk-scores predicts overall survival and can prognosticate patients into high and low risk groups. These findings support the prognostic utility of CT-based radiomics in patients undergoing resection of CRLM and provide rationale for further investigation in a prospective, multi-centre setting.

HPB SO72 - Predicting Major Intra-operative Blood Loss in Patients undergoing Liver Resection: A Machine Learning Radiomics Analysis Using Pre-Operative Liver CTs

Abstract Background Liver resection offers the best opportunity for curing patients with colorectal metastases and is an important treatment option for patients with hepatocellular carcinoma. However, it can be associated with significant morbidity and mortality, with one of the primary reasons being due to intra-operative blood loss which serves as an important predictor of post-operative outcomes. Predicting blood loss pre-operatively would be a powerful tool for clinicians in identifying higher risk patients. In this study, we examine whether radiomic features of the liver can predict major blood loss (>500 mls) alongside clinical and morphometric features in patients undergoing resection for colorectal metastases. Method Patient data was extracted from a prospectively maintained database of individuals undergoing resection for colorectal liver metastases (CRLMs). Radiomic features were extracted from pre-operative portal venous phase CTs. The background liver was randomly segmented in a two-dimensional plane away from cancer areas. Highly correlated features were dropped, and further feature selection techniques applied. Nested cross validation was performed to train and evaluate the machine learning models with the selected radiomic, clinical, and morphometric features. Results Radiomic features (159) were extracted from 106 patients, and 10 radiomic features comprised of first order and texture features, 3 morphometric, and 4 clinical features were selected for the machine learning models. The models demonstrated a robust ability to discriminate between major and minor blood loss. The best performing machine learning model demonstrated an AUC of 0.76 (95% CI 0.7-0.82). Other performance metrics for this model included a mean accuracy of 0.78 and an F1 Score of 0.87 on the validation data. Conclusion Pre-operative CT radiomic, clinical, and morphometric features have shown a modest ability to predict major intra-operative blood loss in patients undergoing hepatic resection for colorectal liver metastases. Further work is needed to validate and refine these models to improve performance. Additionally, we plan to incorporate data from other centres to validate our findings in further experiments to create a robust tool to guide surgical practice in this patient cohort.

Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique.

Deep learning and artificial intelligence offer promising tools for improving the accuracy and efficiency of diagnosing various lung conditions using portable chest x-rays (CXRs). This study explores this potential by leveraging a large dataset containing over 6,000 CXR images from publicly available sources. These images encompass COVID-19 cases, normal cases, and patients with viral or bacterial pneumonia. The research proposes a novel approach called "Enhancing COVID Prediction with ESN-MDFS" that utilizes a combination of an Extreme Smart Network (ESN) and a Mean Dropout Feature Selection Technique (MDFS). This study aimed to enhance multi-class lung condition detection in portable chest X-rays by combining static texture features with dynamic deep learning features extracted from a pre-trained VGG-16 model. To optimize performance, preprocessing, data imbalance, and hyperparameter tuning were meticulously addressed. The proposed ESN-MDFS model achieved a peak accuracy of 96.18% with an AUC of 1.00 in a six-fold cross-validation. Our findings demonstrate the model's superior ability to differentiate between COVID-19, bacterial pneumonia, viral pneumonia, and normal conditions, promising significant advancements in diagnostic accuracy and efficiency.

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization

Parkinson’s Disease (PD) is a neurological condition that worsens with time and is characterized bysymptoms such as cognitive impairment andbradykinesia, stiffness, and tremors. Parkinson’s is attributed to the interference of brain cells responsible for dopamine production, a substance regulating communication between brain cells. The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. The proposed method presents an optimal feature selection technique based on Deep Learning (DL) and the Dynamic Bag of Features Optimization Technique (DBOFOT). Our method combines neural network-based feature extraction with a strong optimization technique to dynamically choose the most relevant characteristics from biological data. Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. With a high accuracy of 0.93, the model accurately identifies 93% of the cases that are categorized as Parkinson’s. Additionally, it has a recall of 0.89, which means that 89% of real Parkinson’s patients are accurately identified. While the recall for Class 0 (Healthy) is 0.75, meaning that 75% of the real healthy cases are properly categorized, the precision decreases to 0.64 for this class, indicating a larger false positive rate.

Evolutionary-based ensemble feature selection technique for dynamic application-specific credit risk optimization in FinTech lending

Evolutionary-based ensemble feature selection technique for dynamic application-specific credit risk optimization in FinTech lending

Revolutionizing Cardio Vascular Disease Diagnosis Through Lasso Regression Model

Cardiovascular diseases (CVDs) are among the most prevalent and fatal health conditions globally, necessitating early and accurate diagnosis to mitigate risk and enhance treatment outcomes. Traditional diagnostic methods often rely on a combination of clinical assessments and static risk models, which can be constrained by their inability to handle the complexity and interrelationships of multiple risk factors. In this research, we propose a novel approach to revolutionizing cardiovascular disease diagnosis through the application of the Lasso (Least Absolute Shrinkage and Selection Operator) regression model, a powerful machine learning technique for feature selection and predictive modeling. The Lasso regression model is particularly advantageous for high-dimensional medical datasets, where multiple clinical features may be correlated. By applying Lasso, we aim to address two critical challenges in cardiovascular disease prediction: identifying the most relevant predictors from large and complex datasets and reducing model over-fitting, which is common when working with a large number of co-variates. Our results demonstrated that the Lasso model significantly improves prediction accuracy when compared to traditional logistic regression and other machine learning models. Lasso regression has the potential to revolutionize cardiovascular disease diagnosis by providing a data-driven, efficient, and interpretable solution that bridges complex medical datasets with practical clinical decision-making. Keywords: Cardio vascular diseases (CVD), Machine learning models, Losso regression model.

Height prediction of individuals with osteogenesis imperfecta by machine learning

BackgroundOsteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of height in individuals with OI was not adequately addressed. In this study, we described the growth patterns and predicted the height of individuals with OI employing multiple machine learning (ML) models. Accurate height prediction enables effective monitoring and facilitates the development of personalized intervention plans for managing OI.MethodThis study included cross-sectional data for 323 participants with OI, and the median height Z-score for OI types I, III and IV were − 0.62 (-5.93 ~ 3.24), -3.97 (-10.44 ~ -0.02) and − 1.64 (-6.67 ~ 2.44), respectively. Based on the cross-sectional data of participants, the height curves across different gender and OI types were plotted and compared. Subsequently, feature selection techniques, specifically the filter and wrapper methods, were employed to identify predictive factors for the height of participants. Finally, multiple machine learning (ML) models were constructed for height prediction, and the performance of each model was systematically evaluated.ResultsThe analysis of height curves revealed that male with OI are significantly taller than female with OI from the age of 14 (p = 0.045), individuals with OI type III are statistically shorter than those with OI types I and IV starting from 3 years old (p = 0.006), and those with OI type IV are statistically shorter than those with OI type I from the age of 10 (p = 0.028). The application of filter and wrapper methods identified gender (p = 0.001), age (p < 0.001), Sillence types (p = 0.007), weight Z-score (p < 0.001) and aBMD Z-score (p = 0.021) as significant predictive factors for height. The optimal performance of predictive models was registered by gradient boosting classifier (GB) (bias = 5.783, accuracy = 92.59%, R2 = 0.828), random forest (RF) (bias = 6.155, accuracy = 90.12%, R2 = 0.788), ensemble machine learning (EML) (bias = 6.250, accuracy = 91.36%, R2 = 0.825) and deep neuron networks (DNNs) (bias = 6.223, accuracy = 90.12%, R2 = 0.821).ConclusionThis study analyzed a large cohort of individuals with OI and provided detailed height patterns across different gender and OI types that are crucial for assessing overall growth. Gender, age, Sillence types, weight Z-score and aBMD Z-score were identified as predictive factors for height. The predictive models of GB, RF, EML and DNNs had higher accuracy to evaluate the height of individuals with OI. This study allows guardians and physicians to timely monitor the height parameters, and facilitate the creation of personalized intervention schedules tailored to the needs of individuals with OI.