Supervised Machine Learning Algorithms Research Articles

Assessing and predicting green gentrification susceptibility using an integrated machine learning approach

ABSTRACT Greenery initiatives, such as green infrastructures (GIs), create sustainable and climate-resilient environments. However, they can also have unintended consequences, such as displacement and gentrification in low-income areas. This paper proposes an integrated machine learning (ML) approach that combines both unsupervised and supervised ML algorithms. First, 35 indicators that contribute to green gentrification were identified and categorised into 7 categories: social, economic, demographic, housing, household, amenities, and GIs. Second, data was collected for all census tracts in New York City. Third, the green gentrification susceptibility was modelled into 6 levels using k-means clustering analysis, which is an unsupervised ML model. Fourth, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) was used to map the census tracts to their green gentrification susceptibility level. Finally, different supervised ML algorithms were trained and tested to predict the green gentrification susceptibility. The results showed that the artificial neural network (ANN) model is the most accurate in classifying and predicting the green gentrification susceptibility with an overall accuracy of 96%. Moreover, the outcomes showed that the Normal Difference Vegetation Index (NDVI), the proximity to GIs, the GIs frequency, and the total area of GIs were identified as the most important indicators to predict green gentrification susceptibility. Ultimately, the proposed approach allows practitioners and researchers to perform micro-level (i.e. on the census-tracts level) predictions and inferences about green gentrification susceptibility. This allows more focused and targeted mitigation actions to be designed and implemented in the most affected communities, thus promoting environmental justice.

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.4 increase in F-1 score from RF.

Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning

Progress has been made in chemotherapy drugs, but drug resistance remains a major challenge in cancer treatment. In clinical practice, although there are existing technologies to assess chemotherapy resistance, there is still a lack of rapid and convenient method to monitor different degrees of drug resistance in cancer cells. Furthermore, the cell morphological changes during the progression of drug resistance remain to be fully understood. In this study, we employed a flow imaging platform based on digital holography (DH) and machine learning to classify cancer cells with different degrees of paclitaxel sensitivity. We extracted 112 morphological features from four datasets of single-cell quantitative phase images of epithelial ovarian cancer (EOC) cells and performed classification training using five supervised machine learning algorithms. Both support vector machine (SVM) and neural network (NN) achieved over 90% classification accuracy. To understand the morphological features associated with drug resistance properties of cancer cells, we applied the SHapley Additive exPlanations (SHAP), a model interpretation framework, to quantify and rank the feature contributions for both classification models. The results reported here about this label-free and high-accuracy assessment of different degrees of drug resistance in cancer cells opens the route to exploitation for monitoring chemotherapy in clinic practices.

Multi-class identification of tonal contrasts in Chokri using supervised machine learning algorithms

This study examines and explores the effectiveness of various Machine Learning Algorithms (MLAs) in identifying intricate tonal contrasts in Chokri (ISO 639-3), an under-documented and endangered Tibeto-Burman language of the Sino-Tibetan language family spoken in Nagaland, India. Seven different supervised MLAs, viz., [Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB)], and one neural network (NN)-based algorithms [Artificial Neural Network (ANN)] are implemented to explore five-way tonal contrasts in Chokri. Acoustic correlates of tonal contrasts, encompassing fundamental frequency fluctuations, viz., f0 height and f0 direction, are examined. Contrary to the prevailing notion of NN supremacy, this study underscores the impressive accuracy achieved by the RF. Additionally, it reveals that combining f0 height and directionality enhances tonal contrast recognition for female speakers, while f0 directionality alone suffices for male speakers. The findings demonstrate MLAs’ potential to attain accuracy rates of 84–87% for females and 95–97% for males, showcasing their applicability in deciphering the intricate tonal systems of Chokri. The proposed methodology can be extended to predict multi-class problems in diverse fields such as image processing, speech classification, medical diagnosis, computer vision, and social network analysis.

Dingo optimization algorithm-based random forests model to evaluate the compressive strength of the concrete at elevated temperatures

The qualities of the materials employed to manufacture concrete are significantly impacted by high temperatures, which results in a noticeable decrease in the material’s strength characteristics. Concrete must be worked very hard and allowed to reach the required compressive strength (fc). Nevertheless, a preliminary estimation of the desired outcome may be made with an outstanding degree of reliability by using supervised machine learning algorithms. The study combined the Dingo optimization algorithm (DOA), Coot bird optimization (COA), and Artificial rabbit optimization (ARO) with Random Forests (RF) evaluation to determine the fc of concrete at high temperatures. The abbreviations used for the combined methods are RFD, RFC, and RFA, respectively. Remarkably, removing the temperature (T) parameter from the input set leads to a remarkable 1100% improvement in the effectiveness index (PI) and normalized root mean squared error (NRMSE), while causing a significant fall in the coefficient of determination (R2). The findings suggest that all RFD, RFC, and RFA have substantial promise in properly forecasting the fc of concrete at high temperatures. More precisely, the RFD algorithm demonstrated exceptional precision with R2 values of 0.9885 and 0.9873 throughout the training and testing stages, respectively. Through a comparison of the error percentages for RFD, RFC, and RFA in error-based measurements, it becomes evident that RFD exhibits an error rate that is about 50% smaller compared to that of RFC and RFA. This prediction is crucial for various industries and applications where concrete structures are subjected to elevated temperatures, such as in fire resistance assessments for buildings, tunnels, bridges, and other infrastructure. By accurately forecasting the compressive strength of concrete under these conditions, engineers and designers can make informed decisions regarding the material’s suitability and performance in high-temperature environments, leading to enhanced safety, durability, and cost-effectiveness of structures.

Sentimental analysis and prediction of socioeconomic disasters tweets by ML and regular expression

Sentiment analysis is a method of analyzing emotions and using text analysis techniques with natural language processing methods. Sentiment analysis uses data from various sources to identify the user’s attitude through different aspects. It is widely used for extracting opinions and recognizing sentiments, which helps Business organizations understand the user’s needs. This paper proposes a simple but compelling sentiment analysis method, showing the combined scores based on positive and negative words. Then, the tweets are categorized as Neutral, Negative, or Positive according to the scores. Sentiment analysis and opinion mining have grown significantly in the last decade. Different studies in this domain try to determine people’s feelings, opinions, and emotions about something or someone. The main objective of this analysis is to determine the sentiment of the review using a machine learning model and then compare the result with the manual review of the data. This would allow researchers to represent and analyze opinions objectively across different domains. A hybrid method that combines a supervised machine learning algorithm with natural language processing techniques is suggested for review analysis. This project aims to find the best model to predict the sentiment of the tweets on airlines. During the research process and considering various methods and variables that should be considered, we found that methods like naïve Bayes and random forest were not fully explored. The proposed system improves an effective and more feasible method for sentimental analysis using machine learning, multinomialNB, linear regression, and regular expression.

CRISHI :A Crop Recommendation System

Abstract: The Crop Recommendation System proposes a mixed model to suggest crops for Indian states, considering various factors such as soil pH, moisture, NPK levels, crop data, and temperature. This suggestion model is constructed as a sample model incorporating supervised machine learning algorithms. The technology- based agricultural crop identifier model aims to enhance farmers crop yield by identifying appropriate crops based on their parameters. The efficacy of the recommendation model lies in its ability to recommend the right crop for specific conditions, which in turn contributes to addressing agricultural and farming challenges and boosting the Indian economy by maximizing crop production. The system also includes a grading process to identify crop quality and signalling the presence of low and high-quality crops. The utilization of a set of classifiers facilitates improved decision-making through multiple classifiers. The assessment process includes a request for decisionmaking to determine the outcomes of the classifiers

Identifying Credit Card Fraudulent Transactions through Machine Learning Analysis

Identifying Credit Card Fraudulent Transactions through Machine Learning Analysis

Prediction of cell cycle distribution after drug exposure by high content imaging analysis using low-toxic DNA staining dye.

Interference in cell cycle progression has been noted as one of the important properties of anticancer drugs. In this study, we developed the cell cycle prediction model using high-content imaging data of recipient cells after drug exposure and DNA-staining with a low-toxic DNA dye, SiR-DNA. For this purpose, we exploited HeLa and MCF7 cells introduced with a fluorescent ubiquitination-based cell cycle indicator (Fucci). Fucci-expressing cancer cells were subjected to high-content imaging analysis using OperettaCLS after 36-h exposure to anticancer drugs; the nuclei were segmented, and the morphological and intensity properties of each nucleus characterized by SiR-DNA staining were calculated using imaging analysis software, Harmony. For the use of training, we classified cells into each phase of the cell cycle using the Fucci system. Training data (n = 7500) and validation data (n = 2500) were randomly sampled and the binary classification prediction models for G1, early S, and S/G2/M phases of the cell cycle were developed using four supervised machine learning algorithms. We selected random forest as the model with the best performance through 10-fold cross-validation; the accuracy rate was approximately 75%-87%. Regarding feature importance, variables expected to be biologically related to the cell cycle, for example, signal intensity and nuclear size, were highly ranked, suggesting the validity of the model. These results showed that the cell cycle can be predicted in cancer cells by simply exploiting the current prediction model using fluorescent images of DNA-staining dye, and the model could be applied for the use of future exvivo drug sensitivity diagnosis.

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Current environmental conditions and human lifestyles have resulted in the emergence of numerous diseases. The medical field generates an enormous amount of new data each year for remote monitoring of patients. Due to increased data growth in the medical and healthcare industries, accurate medical data analysis has been advantageous to early patient care. However, physicians often face challenges in accurately diagnosing diseases in patients far from hospitals. Therefore, utilizing remote patient systems (telemedicine systems) due to the complexities associated with their chronic conditions. On the other hand, predicting illness is also a challenging task. Thus, data extracted from heterogeneous, fast-flowing, and reliable sources is crucial for decision-making and disease prediction. This research paper aims to utilize supervised Machine Learning (ML) techniques to predict chronic diseases such as heart and hypertension based on the patient’s features or symptoms by analyzing patient data collected by sensors and sources enabled by the Internet of Medical Things (IoMT). Supervised ML technology in Hadoop and Spark environments is employed to guarantee that this classification accurately identifies individuals with chronic illnesses. The methods are evaluated using 55,680 patient records to discover the proper match between the data set and the final disease-predicted result. The results demonstrate that the proposed procedure employing the Decision Tree (DT) algorithm is 94% accurate, and DT outperforms the other four ML algorithms. This includes the Support Vector Machine (SVM), a Naive Bayes (NB) model, Random Forest (RF), and Logistic Regression (LR) in terms of both performance and accuracy metrics (precision, recall, and F-score).

Software Bug Prediction Using Machine Learning

Software bug prediction is a crucial aspect of software development and maintenance as it directly impacts the overall success of the software. By identifying potential bugs early on, software quality, reliability, and efficiency can be improved while also helping to reduce costs. Creating a reliable bug prediction model is a complex task, with various techniques proposed in the literature. In this paper, a bug prediction model utilizing machine learning algorithms is introduced. Three supervised machine learning algorithms have been incorporated to forecast future software faults using historical data. The evaluation process revealed that Machine Learning algorithms, specifically Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Networks (ANNs), can be effectively utilized with a high level of accuracy. Additionally, a comparison measure was implemented to assess the proposed prediction model against other methods. The findings indicated that the Machine Learning approach outperformed other techniques in terms of performance. Key Words: Bug Prediction, Reliability, Efficiency, Quality, Machine Learning, Performance Metrics

Fake News Detection Model

The proliferation of fake news across social media and various other platforms is a significant concern due to its potential to cause widespread social and national harm. Extensive research efforts are currently underway to identify and combat this issue. This study conducts an in-depth analysis of research pertaining to the detection of fake news, examining traditional machine learning models to determine the most effective approach. By leveraging supervised machine learning algorithms, a model will be developed to classify news articles as either true or false. This model will utilize tools such as Python's scikit-learn and NLP for textual analysis. The process will involve feature extraction and vectorization, with a focus on utilizing Python's scikit-learn library for text data tokenization and feature extraction, leveraging tools like Count Vectorizer and Tiff Vectorizer. Additionally, feature selection methods will be employed to identify the most relevant features for optimal precision, as determined by the results of the confusion matrix

Mobile network traffic analysis based on probability-informed machine learning approach

The paper proposes an approach to the joint use of statistical and machine learning (ML) models to solve the problems of the precise reconstruction of historical events, real-time detection of ongoing incidents, and the prediction of future quality of service-related occurrences for prospective development of the modern networks. For forecasting, a regression version of the deep Gaussian mixture model (DGMM) is introduced. First, the preliminary clustering based on the finite normal mixtures is performed. This information is then used as an input for some supervised ML algorithm. It is the basic concept of the probability-informed ML approach in the field of telecommunications networks. Using the real-world datasets from a Portuguese mobile operator as well as public cellular traffic data, the article compares this approach with methods such as random forests, support vector machine regression, gradient boosting and LSTM. Vector autoregression, informed by the parameters of the generalized gamma (GG) distribution, which has also been successfully used to reconstruct past traffic patterns, is also used as a benchmark. We demonstrate that DGMM-based regression is 6.82–22.8 times faster than LSTM for the dataset. Moreover, DGMM-based regression can achieve better results for the most important traffic characteristics (average and total traffic, the number of users). For metrics MAPE and RMSE, it surpasses the results of statistical methods up to 46.7% (RMSE) and 91.5% (MAPE) (median increases are 28.0% and 80.1%, respectively), as well as for ML methods up to 13.0% (RMSE) and 35.7% (MAPE) (median increases are 0.39% and 2.5%, respectively). Thus, the use of a probability-informed approach for telecommunication data seems optimal for the computational speed and accuracy trade-off. Also, we introduce a novel statistical hypothesis testing method based on GG distribution for detecting suspected anomalies in traffic.

Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions

BackgroundClassification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model’s ability to extrapolate.ResultsWe present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process.ConclusionsNoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/. The implementation detailed in this paper corresponds to the version 0.2.1 release of the software.

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls.

Prediction of students’ performance in online learning using supervised machine learning

PurposeThis study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course.Design/methodology/approachThe exploration and visualisation of the data were first carried out to gain a better understanding of the students, the course(s) each student was enrolled in and each course’s virtual learning resources. Following this, the construction of the social network graphs was performed to depict how each student behaved online before the degree centralities were computed for each of the nodes in a social network graph. Data pre-processing to assign labels based on the final result a student obtained in a course was then performed before we trained and tested models to predict which students did or did not graduate.FindingsThe study’s findings demonstrate that the constructed predictive model has good performance, as shown by the accuracy, precision, recall and f-measure metrics. The outcomes also showed that students’ use of online resources is a crucial element that influences how well they perform in their academics.Originality/valueThe similarity index is as low as 9%.

Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

On-Demand MXene-Coupled Pyroelectricity for Advanced Breathing Sensors and IR Data Receivers.

MXene-inspired two-dimensional (2D) materials like Ti3C2Tx are widely known for their versatile properties, including surface plasmon, higher electrical conductivity, exceptional in-plane tensile strength, EMI shielding, and IR thermal properties. The MXene nanosheets coupled poly(vinylidene fluoride) (PVDF) nanofibers with d33 ∼-26 pm V-1 are able to capture the smaller thermal fluctuation due to a superior pyroelectric coefficient of ∼130 nC m-2 K-1 with an improved (∼7 times with respect to neat PVDF nanofibers) pyroelectric current figure of merit (FOMi). The significant enhancement of the pyroelectric response is attributed to the confinement effect of 2D MXene (Ti3C2Tx) nanosheets within PVDF nanofibers, as evidenced from polarized Fourier transform infrared (FTIR) spectroscopy and scanning probe microscopy (SPM). In subsequent studies, the practical applications of self-powered pyroelectric sensors of MXene-PVDF have been demonstrated. The fabricated flexible, hydrophobic pyroelectric sensor could be utilized as an excellent pyroelectric breathing sensor, a proximity sensor, and an IR data transmission receiver. Further, supervised machine learning algorithms are proposed to distinguish different types of breathing signals with ∼98% accuracy for healthcare monitoring purposes.

From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL).A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models.After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors.Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.