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Identifying diseases symptoms and general rules using supervised and unsupervised machine learning

SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Sogandi, Fatemeh Univ Torbat Heydarieh Dept Ind Engn Torbat Heydarieh Iran

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time

关键词： Diseases symptoms Classification methods Association rules Apriori algorithm machine learning algorithms

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Applying machine learning to Estimate the Effort and Duration of Individual Tasks in Software Projects

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IEEE ACCESS 2023年 11卷 89933-89946页

作者： Sousa, Andre Veloso, Daniel Goncalves, Henrique Faria, Joao Pascoal Mendes-Moreira, Joao Graca, Ricardo Gomes, Duarte Castro, Rui Nuno Henriques, Pedro Castro Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal Fraunhofer Portugal AICOS P-4200135 Porto Portugal Strongstep P-4100429 Porto Portugal

Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to erro

关键词： Task analysis Software Software algorithms Maximum likelihood estimation Prediction algorithms Predictive models machine learning algorithms Software development management Project management Effort estimation duration estimation machine learning task estimation software projects

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Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein

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ANNALS OF HEPATOLOGY 2024年第6期29卷 101540-101540页

作者： Xu, Yuan Zhang, Bei Zhou, Fan Yi, Ying-ping Yang, Xin-Lei Ouyang, Xiao Hu, Hui Nanchang Univ Med Big Data Ctr Affiliated Hosp 2 Nanchang Peoples R China Nanchang Univ Dept Gastroenterol Affiliated Hosp 2 Nanchang Peoples R China Nanchang Univ Dept Hepatobiliary Surg Affiliated Hosp 2 Nanchang Peoples R China Quiclin Technol Co Ltd Nanchang Peoples R China

Introduction and Objectives: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. Patients and Methods: A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80%) to develop the ML models. Results: Twelve independent risk factors for HCC were identified using Gaussian na & iuml;ve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/ mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium <= 3.5 mmol/L, direct bilirubin >6.8 mmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sen

关键词： Hepatocellular carcinoma Alpha-fetoprotein machine learning algorithms Prediction model

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machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale

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Resilient Cities and Structures 2024年第1期3卷 84-102页

作者： Sanjeev Bhatta Xiandong Kang Ji Dang Graduate School of Civil and Environmental Engineering Saitama UniversitySaitamaJapan Beijing Earthquake Agency BeijingChina

This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the *** the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history *** machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and *** random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged *** structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,***,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher *** places with no earthquake recording station/device,it is difficult to have ground motion *** that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground *** random forest regressor(RFR)achieved better results than other regression models on testing and validation ***,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation *** the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an *** damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

关键词： Seismic damage prediction Ground motion parameter machine learning algorithms Nonlinear time history analysis RC buildings

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Analysis of learning algorithms for Predicting Carbon Emissions of Light-Duty Vehicles

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 584-589页

作者： Kale, Rashmi B. Shaikh, Nuzhat Faiz Smt Kashibai Navale Coll Engn SPPU Dept Comp Engn Pune Pune India Wadia Coll Engn Dept Comp Engn Pune Pune India

research presents a comparative analysis of different learning methods developed for the prediction of carbon emissions from light-duty vehicles. With the growing concern over environmental sustainability, accurate prediction of carbon emissions is vital for developing effective mitigation strategies. The work assesses the performance of various algorithms trained on vehicle-specific data attributes to predict the emission patterns of a fuel type of different light duty models. This work uses two realtime petrol and diesel datasets collected by CariQ app and device. Canada government dataset is also used from the online repository for prediction of the vehicle emission. The evaluation is based on their predictive accuracy. The findings reveal insights into the effectiveness of different learning techniques in accurately estimating carbon emissions from vehicles, providing valuable guidance for policymakers and researchers in the field of environmental sustainability and transportation planning.

关键词： Carbon - Carbon emission machine learning algorithms CariQ carbon emission dataset An Air Quality Index (AQI)

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machine learning enables automated screening for systematic reviews and meta-analysis in urology

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WORLD JOURNAL OF UROLOGY 2024年第1期42卷 396-396页

作者： Menold, H. S. Wieland, V. L. S. Haney, C. M. Uysal, D. Wessels, F. Cacciamani, G. C. Michel, M. S. Seide, S. Kowalewski, K. F. Heidelberg Univ Univ Med Ctr Mannheim Dept Urol & Urol Surg Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany Univ Leipzig Dept Urol Leipzig Germany Univ Med Ctr Mannheim Intelligent Syst & Robot Urol ISRU DKFZ Hector Canc Inst Mannheim Germany German Canc Res Ctr Heidelberg Germany Univ Southern Calif USC Inst Urol Los Angeles CA USA Bohringer Ingelheim Ingelheim Germany

PurposeTo investigate and implement semiautomated screening for meta-analyses (MA) in urology under consideration of class *** learning algorithms were trained on data from three MA with detailed information of the screening process. Different methods to account for class imbalance (Sampling (up- and downsampling, weighting and cost-sensitive learning), thresholding) were implemented in different machine learning (ML) algorithms (Random Forest, Logistic Regression with Elastic Net Regularization, Support Vector machines). Models were optimized for sensitivity. Besides metrics such as specificity, receiver operating curves, total missed studies, and work saved over sampling were *** training, models trained after downsampling achieved the best results consistently among all algorithms. Computing time ranged between 251 and 5834 s. However, when evaluated on the final test data set, the weighting approach performed best. In addition, thresholding helped to improve results as compared to the standard of 0.5. However, due to heterogeneity of results no clear recommendation can be made for a universal sample size. Misses of relevant studies were 0 for the optimized models except for one *** will be necessary to design a holistic methodology that implements the presented methods in a practical manner, but also takes into account other algorithms and the most sophisticated methods for text preprocessing. In addition, the different methods of a cost-sensitive learning approach can be the subject of further investigations.

关键词： Urology Meta-analysis Systematic reviews machine learning algorithms

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Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm

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Geoscience Frontiers 2023年第4期14卷 200-219页

作者： Xiao-Ming Li Yi-Xin Zhang Zhan-Ke Li Xin-Fu Zhao Ren-Guang Zuo Fan Xiao Yi Zheng School of Earth Resources China University of GeosciencesWuhanHubei Province 430074China School of Computer Science China University of GeosciencesWuhanHubei Province 430078China State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesWuhan 430074China School of Earth Sciences and Engineering Sun Yat-Sen UniversityZhuhaiGuangdong Province 519000China

Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)***,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit ***,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn *** this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal *** Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal *** further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification ***’s feature importance and permutation feature importance were adopted to evaluate the element significance for ***,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and *** results presented here show that Mn,Co,and Ge display significant impacts on classifica

关键词： Discrimination Pb-Zn deposit Sphalerite trace elements machine learning algorithms Feature analysis

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machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

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RENAL FAILURE 2024年第1期46卷 2316267页

作者： Gao, Tianyun Nong, Zhiqiang Luo, Yuzhen Mo, Manqiu Chen, Zhaoyan Yang, Zhenhua Pan, Ling Guangxi Med Univ Dept Nephrol Affiliated Hosp 1 Nanning Peoples R China Guangxi Med Univ Dept Crit Care Med Affiliated Hosp 1 Nanning Peoples R China Guangxi Med Univ Dept nephrol Affiliated Hosp 1 6 Shuangyong Rd Nanning Guangxi Peoples R China

ObjectivesThis study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning *** who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP).ResultsA total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor *** RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.

关键词： Sepsis acute kidney injury prediction model of prognosis machine learning algorithms

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An Analysis of Semi-Supervised machine learning in Electrical machines

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IEEE ACCESS 2025年 13卷 82927-82959页

作者： Arvind, V. Raju Shyamsharan, S. Gurunathan, Poorvajaa Kumba, Krishna Ra, Nawin Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

This research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate characteristics of electrical machines. SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliability, and maintenance by leveraging labeled and unlabeled data. The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. Moreover, the document discusses the specific difficulties associated with this merger and offers remedies to improve the utilization of SSML in this significant area. A detailed table summarizes various methods and emphasizes their role in furthering machine learning within the realm of electrical machines.

关键词： Data models machine learning Training Predictive models Accuracy Prediction algorithms Predictive maintenance Semisupervised learning machine learning algorithms Supervised learning Electrical machines machine learning generative models graph based methods self training co-training

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Fraud Detection in Banking Data by machine learning Techniques

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IEEE ACCESS 2023年 11卷 3034-3043页

作者： Hashemi, Seyedeh Khadijeh Mirtaheri, Seyedeh Leili Greco, Sergio Kharazmi Univ Fac Engn Dept Elect & Comp Engn Tehran *** Iran Univ Calabria Dept Informat Modeling Elect & Syst Engn I-87036 Arcavacata Di Rende Italy

As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.

关键词： Fraud Credit cards machine learning algorithms Measurement Deep learning Bayes methods Logistics Bayesian optimization data Mining deep learning ensemble learning hyper parameter unbalanced data machine learning

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