Ability Of Machine Learning Algorithms Research Articles

Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning

AbstractObjectivesDocumentation and billing are important and time‐consuming parts of an otolaryngologist's work. Given advancements in machine learning (ML), we evaluated the ability of ML algorithms to use operative notes to classify rhinology procedures by Current Procedural Terminology (CPT®) code. We aimed to assess the potential for ML to replicate rhinologists' completion of their administrative tasks.Study DesignRetrospective cohort study.SettingUrban tertiary hospital.MethodsA total of 594 operative notes from rhinological procedures across six CPT codes performed from 3/2017 to 4/2022 were collected from 22 otolaryngologists. Text was preprocessed and then vectorized using CountVectorizer (CV), term frequency‐inverse document frequency, and Word2Vec. The Decision Tree, Support Vector Machine, Logistic Regression and Naïve Bayes (NB) algorithms were used to train and test models on operative notes. Model‐classified CPT codes were compared to codes assigned by operating surgeons. Model performance was evaluated by area under the receiver operating characteristic curve (ROC‐AUC), precision, recall, and F1‐score.ResultsPerformance varied across vectorizers and ML algorithms. Across all performance metrics, CV and NB was most overall the best combination of vectorizer and ML algorithm across CPT codes and produced the single best AUC, 0.984.ConclusionsIn otolaryngology applications, the performance of basic ML algorithms varies depending on the context in which they are used. All algorithms demonstrated their ability to classify CPT codes well as well as the potential for using ML to replicate rhinologists' completion of their administrative tasks.

Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.

In Europe, concentrations of ∆9-tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter

In the present age, marked by data-driven advancements in various fields, the importance of machine learning (ML) holds a prominent position. The ability of ML algorithms to resolve complex patterns and extract insights from large datasets has solidified its transformative potential in various scientific domains. This paper introduces an innovative application of ML techniques in the domain of radiation dosimetry. Specifically, it shows the applicability of ML in estimating the radiation dose received by occupational workers. This estimation is expressed in terms of personal dose equivalent, and it involves the utilization of thermoluminescence signals emitted by CaSO4:Dy-based personnel monitoring badges. To estimate personal dose equivalent, three-stage algorithm driven by ML models is proposed. This algorithm systematically identifies the photon energy ranges, calculates the average photon energy, and determines personal dose equivalent. By implementing this approach to the conventional three-element dosimeter, the study overcomes existing limitations and enhances accuracy in dose estimation. The algorithm demonstrates 97.8% classification accuracy in discerning photon energy ranges and achieves a coefficient of determination of 0.988 for estimating average photon energy. Importantly, it also reduces the coefficient of variation of relative deviations by up to 6% for estimated personal dose equivalent, compared to existing algorithms. The study improves accuracy and establishes a new methodology for evaluating radiation exposure to occupational workers using conventional thermoluminescent dosimeter badge.

Machine learning studies for liquid crystal texture recognition

ABSTRACT Machine learning techniques such as local binary pattern algorithm and convolutional neural network were applied to study textures of liquid crystal compound (S)-4’-(1-methyloctyloxycarbonyl) biphenyl-4-yl 4-[7-(2,2,3,3,4,4,4-heptafluorobutoxy) heptyl-1-oxy]-benzoate (3F7HPhH7). This compound exhibits in its polymorphism several smectic phases (smectic A, ferroelectric smectic C and antiferroelectric smectic C) and two glass states of antiferroelectric smectic C phase, which textures are difficult to distinguish. Proof-of-concept evidence is provided, demonstrating the ability of machine learning algorithms to identify transition temperatures and the respective phases involved. The article describes the procedure for preparing a dataset of textures obtained from polarised microscopy for classification using convolutional neural networks, especially in cases where the image dataset is unbalanced. It utilises the feature of their self-similarity and image augmentation, particularly the cropping procedure. A Kolmogorov–Smirnov test was conducted to check if selected images do not carry the same information.

Intrusion Detection Recording System with Biometric Lock

The spread of COVID-19 in the entire world has put humankind in danger. The assets of probably the biggest economies are worried because of the enormous infectivity and contagiousness of this illness. The ability of machine learning algorithms to predict the number of possible COVID-19 patients is generally seen as a potential challenge to mankind. The undermining components of COVID-19 were determined using four normal estimating models: Support Vector Machine (SVM), least total shrinkage, and determination administrator (LASSO), linear regression (LR). Any one of the models makes three types of predictions, such as the number of newly infected occurrences, the number of passings, and the rate of recoveries, but they cannot predict the exact result for the patients. To defeat the issue, the Proposed strategy utilizing exponential smoothing (ES) The number of cases of COVID-19 and the impact of COVID-19 preventive steps including certain social insulation and latch on infectious diseases was expected in the next 30 days to come.

Transformer network for data imputation in electricity demand data

Load forecasting necessitates a significant amount of smart meter data. Several elements in this process, including device malfunctions and signal transmission issues, produce missing data gaps. Missing values in the dataset significantly influence the learning ability of machine learning algorithms, and they must be infilled before proceeding with any statistical analysis. This paper investigates the handling of missing values in demand data, and a new approach is developed for improving the performance of demand analytics, such as energy forecasting. The proposed model uses a transformer neural network to impute the missing values at various rates in the demand profile. Our model uses a k-means algorithm to fill in the missing values with proxy values in the dataset. The model is applied to two case-study residential house located in Cornwall and Fintry, United Kingdom. The developed algorithm is assessed for it potential for infilling missing values for three widely understood missing value scenarios: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). The proposed model's imputed outputs are compared to the original dataset to assess model performance. The performance of the framework is compared with a selection of widely used statistical and machine learning models. The proposed transformer model shows significant improvements over the common linear method in all three scenarios (with 30% missing values), with percentage improvements ranging from approximately 49.71% to 57.52% for Cornwall dataset.

On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features-mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator-were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases.

Advanced well stimulation selection with gradient boosting

To ensure the required level of production of brown fields, it is necessary to plan and implement an efficient program of well stimulation in a timely manner. This program has a significant impact on the further development of the oil field in terms of its productivity and economics. Nowadays, the decisions about well stimulations are made by experts mostly based on their experience and the company’s heuristics. In the work, a novel approach to fast, accurate, and computationally efficient selection of candidates for well stimulations was presented. We explored the ability of Machine Learning algorithms to solve the problem. The predictive pipeline was built on the basis of production and pressure time series of historical data. We developed and applied a comprehensive prepossessing workflow to a real field data to prepare a training data set. Finally, two Gradient-Boosting models were developed, tuned, trained and validated. The first model was used for prediction of the necessity for the well stimulation and the second model—for identification of the required treatment type. Blind test of the models was provided. It resulted in a 0.80 recall score and 0.79 balanced accuracy score.

Obstructive Sleep Apnea Prediction Using Machine Learning

Abstract: A common sleep problem called obstructive sleep apnea (OSA) is characterized by periodic breathing pauses while you're asleep. For OSA to be effectively treated and related health concerns to be avoided, an early and precise diagnosis is essential. Machine learning approaches have become effective tools in recent years for managing and diagnosing OSA. The research that used machine learning techniques for OSA detection and prediction are reviewed in detail in this publication. A large dataset made up of polysomnography recordings and clinical data from OSA sufferers was gathered in order to achieve this. Using this dataset, many machine learning models were trained to categorize the severity levels of OSA or predict the occurrence of OSA, including support vector machines, random forests, and deep neural networks. The findings demonstrated the powerful ability of machine learning algorithms to recognize OSA and distinguish between various severity levels. These models outperformed conventional diagnostic techniques in terms of high sensitivity and specificity rates. To further shed insight on the underlying patterns and processes of the illness, feature selection approaches were used to determine the most pertinent physiological parameters for OSA detection. The effectiveness and precision of screening and therapy strategies may be improved by incorporating machine learning algorithms into OSA diagnosis. This study advances our understanding of the use of machine learning in sleep medicine and paves the door for the creation of automated and individualized OSA diagnostic tools.

Machine Learning-Based Classification to Disentangle EEG Responses to TMS and Auditory Input.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) offers an unparalleled opportunity to study cortical physiology by characterizing brain electrical responses to external perturbation, called transcranial-evoked potentials (TEPs). Although these reflect cortical post-synaptic potentials, they can be contaminated by auditory evoked potentials (AEPs) due to the TMS click, which partly show a similar spatial and temporal scalp distribution. Therefore, TEPs and AEPs can be difficult to disentangle by common statistical methods, especially in conditions of suboptimal AEP suppression. In this work, we explored the ability of machine learning algorithms to distinguish TEPs recorded with masking of the TMS click, AEPs and non-masked TEPs in a sample of healthy subjects. Overall, our classifier provided reliable results at the single-subject level, even for signals where differences were not shown in previous works. Classification accuracy (CA) was lower at the group level, when different subjects were used for training and test phases, and when three stimulation conditions instead of two were compared. Lastly, CA was higher when average, rather than single-trial TEPs, were used. In conclusion, this proof-of-concept study proposes machine learning as a promising tool to separate pure TEPs from those contaminated by sensory input.

Machine learning enhances prediction of plants as potential sources of antimalarials.

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families - Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) - and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms - Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks - and compare these to two ethnobotanical selection approaches - based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best - attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

An Intelligent Intrusion Detection System for Internet of Things Attack Detection and Identification Using Machine Learning

The usability and scalability of Internet of things (IoT) technology are expanding in such a way that they facilitate human living standards. However, they increase the vulnerabilities and attack vectors over IoT networks as well. Thus, more security challenges could be expected and encountered, and more security services and solutions should be provided. Although many security techniques propose and promise good solutions for that intrusion detection systems IDSs still considered the best. Many works proposed machine learning (ML)-based IDSs for IoT attack detection and classification. Nevertheless, they suffer from two main gaps. First, few of the works utilized or could analyze an up-to-date version of IoT-based attack behaviors. Second, few of the works can be considered as multi-class attack detection and classification. Therefore, this work proposes an intelligent IDS (IIDS) by exploiting the ability of ML algorithms to classify and identify malicious from benign behaviors among IoT network packets. Three ML classifier algorithms are investigated, which are K-Nearest Neighbor, support vector machine, and artificial neural network. The developed models have been trained and tested as binary and multi-class classifiers against 15 types of attacks and benign. This work employs an up-to-date dataset known as IoT23, which covers millions of malicious and benign behaviors of IoT-connected devices. The process of developing the proposed IIDSs goes under different preprocessing phases and methods, such as null value solving, SMOTE method for the imbalanced datasets, data normalization, and feature selections. The results present IIDSs as good binary and multi-class classifiers even for zero-day attacks.

Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives

The success of machine learning (ML) techniques in the formerly difficult areas of data analysis and pattern extraction has led to their widespread incorporation into various aspects of human life. This success is due in part to the increasing computational power of computers and in part to the improved ability of ML algorithms to process large amounts of data in various forms. Despite these improvements, certain issues, such as privacy, continue to hinder the development of this field. In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to clients to be trained locally, and then only the models themselves, without associated data, are sent back to the server to combine the different locally trained models into a single global model. In this respect, the aggregation algorithms play a crucial role in the federated learning process, as they are responsible for integrating the knowledge of the participating clients, by integrating the locally trained models to train a global one. To this end, this paper explores and investigates several federated learning aggregation strategies and algorithms. At the beginning, a brief summary of federated learning is given so that the context of an aggregation algorithm within a FL system can be understood. This is followed by an explanation of aggregation strategies and a discussion of current aggregation algorithms implementations, highlighting the unique value that each brings to the knowledge. Finally, limitations and possible future directions are described to help future researchers determine the best place to begin their own investigations.

Utilizing artificial neural networks to predict the asphalt pavement profile temperature in western Europe

The temperature profile significantly influences the structural performance of asphalt pavement since it influences various mechanical parameters such as stiffness, strength, and fatigue life. The present study aims to achieve two main objectives. Firstly, to explore the potential of Machine Learning (ML) approaches in predicting asphalt pavement profile temperature in the western Europe climate. Secondly, to determine the impact of different weather parameters on the effectiveness of the prediction models. Therefore, three ML algorithms are used to develop asphalt temperature prediction models: autoencoder network, Feedforward Neural Network (FFNN), and Long Short-Term Memory (LSTM).The performance of different ML algorithms is assessed by comparing the accuracy of the prediction models. The analysis results indicated that the autoencoder network is the most accurate algorithm in predicting asphalt pavement temperatures at various depths. Regarding the weather parameters' input dimension, a direct relationship was observed between the number of input dimensions and the performance of the prediction models, where the model using all weather data performs best. The accuracy of the developed ML and a validated FE model were compared with the collected experimental data in terms of Mean Absolute Error (MAE). The average MAEs were calculated between 0.25 and 0.31 °C and 1.05–1.19 °C for ML and FEM approaches, respectively. The results showed the ability of ML algorithms to predict the temperature of asphalt pavement with a higher degree of accuracy compared to numerical models with no extra information about material properties or boundary conditions.

AMS Intrusion Detection Method Based on Improved Generalized Regression Neural Network

<p>The smart grid integrates the computer network with the traditional power system and realizes the intelligentization of the power grid. The Advanced Measurement System (AMS) interconnects the power system with the user, realizes the two-way interaction of data and information between the power supplier and the user, and promotes the development of the smart grid. Therefore, the safe operation of AMS is the key to the development of the smart grid. As smart grids and computer networks become more and more closely connected, the number of cyberattacks on AMS continues to increase. Currently, AMS intrusion detection algorithms based on machine learning are constantly being proposed. Machine learning algorithms have better learning and classification capabilities for small sample data, but when faced with a large amount of high-dimensional data information, the learning ability of machine learning algorithms is reduced, and the generalization ability is reduced. To enhance the AMS intrusion detection algorithm, this paper uses a Generalized Regression Neural Network (GRNN) to identify attack behaviors. GRNN has strong non-linear mapping ability, is suitable for unstable data processing with small data characteristics, has good classification and prediction ability, and has been widely used in power grid systems. Aiming at the existing problems, this paper proposes an upgraded generalized regression neural network AMS intrusion detection method DBN-DOA-GRNN. Based on the feature extraction and dimensionality reduction of the data by DBN, GRNN is used for data with less feature information in learning classification. In addition, to improve the detection effect of the method, the Drosophila Optimization Algorithm (DOA) is used to optimize the parameters of GRNN to reduce the influence of random parameters on the detection results, improve the detection accuracy of this method on small-scale sample data, and thereby improve the detection performance of the AMS intrusion detection algorithm. The proposed method archives an accuracy of 87.61%, 3.10% false alarm rate, and 96.9 precision rate.</p> <p> </p>

Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis

Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue; RGB) sensor to identify invasive Phragmites australis in multiple Minnesota wetlands using object-based image analysis (OBIA) and machine learning (ML) algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The addition of a post-ML classification OBIA workflow was tested to determine if ML classifications can be improved using OBIA techniques. Results from each ML algorithm were compared across study sites both with and without the post-ML OBIA workflow. ANN was identified as the best classifier when not incorporating a post-ML OBIA workflow with a classification accuracy of 88%. Each of the three ML algorithms achieved a classification accuracy of 91% when incorporating the post-ML OBIA workflow. Results from this study suggest that a post-ML OBIA workflow can increase the ability of ML algorithms to accurately identify invasive Phragmites australis and should be used when possible. Additionally, the decision of which ML algorithm to use for Phragmites mapping becomes less critical with the addition of a post-ML OBIA workflow.

A GENERIC MACHINE LEARNING-BASED FRAMEWORK FOR PREDICTIVE MODELING OF LAND SURFACE TEMPERATURE

Abstract. In the realm of data analytics, machine learning (ML) is one of the most successful techniques for making predictions. The ability of ML algorithms has also been studied in various aspects of land surface temperature (LST) besides its retrieval. The few investigations on LST retrieval using ML algorithms suggested that it may potentially obtain the LST values incorporating relevant variables of land surface parameters; however, the variables and ML models used differ, and so do their accuracies. The accuracy of the model is affected by the variable's contribution, its quality and quantity, and the fulfilment of each technique's assumptions. Hence this study provides a wide range of LST indicators to be employed for LST retrieval using a widely used ML algorithm, random forest. The ML algorithm framework for LST prediction is illustrated with significant spectral indices and terrain parameters across the highly industrialised district of Jharkhand, India. With the exception of one (aspect) variable, the analysis shows that all 20 variables that were included as independent factors were significant and equally contributed to the model. The model built with all the variables including the aspect of the terrain obtained an RMSE of 1.13 degree Celsius and R2 of 0.48. However, after the removal of aspect, the model obtained an R2 of 0.89 and RMSE of 0.74 °C. The performance of the model on consecutive removal of lesser significant variables are evaluated and the study made clear how crucial it is to consider several environmental or land-use factors that could be pertinent to LST.

APRENDIZAGEM DE MÁQUINA PARA IDENTIFICAÇÃO DE PLANTAS DE SOJA SOB ATAQUE DE INSETOS USANDO DADOS HIPERESPECTRAIS

The integration between the areas of remote sensing and machine learning has allowed an advance in the way of mapping agricultural fields and monitoring crops. This work investigates the ability of machine learning algorithms to classify soybean plants under insect attack, using reflectance spectroscopy measurements collected at the leaf level. To this end, tests were developed with different algorithms using a set of 991 spectral curves referring to healthy soybean plants under attack by pests, collected in eight consecutive days. These curves were measured by the EMBRAPA team, using a portable spectroradiometer, which records in the range of 350 to 2500 nm. Such curves were, initially, pre-processed to remove the regions of atmospheric absorption by water vapor, and then subdivided into a set of training, validation and testing of the machine learning algorithms. The Google Collabs interpreter was used and the algorithms were written in Python language, using libraries such as Skit Sklearn. Among the algorithms used, there are Random Forest, Decision Tree, Support Vector Machine, Logistic Regression and Extra-Tree. The Extra-tree has better performance (F1-score = 80.40%; precision = 81%; recall = 80%) in the proposed task. It is concluded that it is possible to process reflectance spectroscopy measurements with machine learning algorithms to monitor insect attack on soybean plants. It is recommended that the applied approach be tested in other cultures.

PS-BPP03-1: PREDICTIVE ABILITY FOR NEW-ONSET CHRONIC KIDNEY DISEASES BY MACHINE LEARNING ALGORITHM USING HEALTH CHECKUP DATA

Background: In Japan, many data on relatively healthy individuals exist because specific health checkups are conducted annually for individuals aged 40 and older for the purpose of early detection and intervention of lifestyle-related diseases. Efforts to effectively utilize these data to predict the future development of chronic kidney disease (CKD) are important from the perspective of improving prognosis and quality of life, as well as healthcare economics. Aim: We aimed to evaluate the predictive ability of machine learning algorithms for newly developed CKD using health checkup data. Methods: We included individuals aged 30–69 years who had a health checkups between April 2008 and March 2018 with baseline (year X) and 5-year (year X + 5) data. CKD was defined as < 60 ml/min/1.73m2, and those with hypertension in year X were excluded. Three different machine learning algorithms, including Random Forest, Logistic Regression, and Neural Network, were evaluated to predict the development of new-onset CKD at year X + 5 from baseline (year X) data, and the AUCs of the ROC curve were used to assess the predictive ability of the models. Explanatory variables included physical measurements, blood pressure and pulse rate, laboratory values, lifestyle-related indicators, and medication history. Results: A total of 58,418 participants (age 53.8 ± 10.2 years, male 49.9%) were included in this study. Among them, 3,997 individuals (6.8%) had newly developed hypertension in year X + 5. The indices of predictive ability were as follows: Random Forrest, AUC 0.88, accuracy 0.93, recall 0.99, precision 0.93; Logistic Regression, AUC 0.89, accuracy 0.93, recall 0.99, precision 0.94; Neural Network, AUC 0.89, accuracy 0.93, recall 0.99, precision 0.93. The variables with the highest importance features were baseline creatinine level (0.49), age (0.24), systolic blood pressure (0.07), and anti-hypertensive medication (0.06). Conclusions: The AUCs of the model predicting the development of CKD after 5 years ranged from 0.88 to 0.89, which were generally superior to previously reported risk models using statistical methods. Baseline creatinine levels, age, and blood pressure were found to contribute significantly to new-onset CKD.