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Comparative Analysis of Machine Learning algorithms for CKD Risk prediction

IEEE ACCESS 2024年 12卷 171205-171220页

作者： Yang, Weilin Ahmed, Nasim Barczak, Andre L. C. Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Bond Univ Ctr Data Analyt Gold Coast Qld 4226 Australia

Chronic Kidney Disease (CKD) remains a significant global health challenge, with increasing prevalence and a substantial impact on patient quality of life. Early and accurate prediction of CKD risk is crucial for timely intervention and management. This study presents a comprehensive comparative analysis of both machine learning and deep learning algorithms applied to predict CKD risk. The research involved the application of eight traditional machine learning algorithms: Naive Bayes, K-nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, AdaBoost, and XGBoost, each implemented on a CKD dataset retrieved from the UCI data repository. Furthermore, three neural network-based algorithms, Artificial Neural Network (ANN), Simple Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) were used to compare to the traditional algorithms. This comparative study not only assessed each algorithm's performance in terms of accuracy, precision, recall, and F1 score but also examined their computational efficiency and applicability in real-world clinical settings. All eleven algorithms were trained with three versions of the dataset. The first version kept the original unbalance between classes and used KNN imputation to fill up missing values (unbalanced). The second dataset used SMOTENC to create new samples to balance the dataset (balanced). The third dataset used feature selection to choose 14 features from the original 24. The results showed that there is almost no performance difference among the classifiers produced with the balanced, unbalanced and feature selection datasets. This means that the best algorithms for this task are the ones with short training and testing runtime, namely RF, SVM, AdaBoost and XGBoost. The experiments also showed that the neural network-based algorithms had no performance advantage and were slower to train due to the small size of samples available in the original dataset.

关键词： Chronic kidney disease Predictive models Accuracy Random forests Machine learning algorithms Machine learning prediction algorithms Nearest neighbor methods Support vector machines Classification algorithms machine learning deep learning risk prediction healthcare analytics

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Tensor-Based Baum-Welch algorithms in Coupled Hidden Markov Model for Responsible Activity prediction

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 2924-2937页

作者： Zhang, Shunli Yang, Laurence T. Zhang, Yue Lu, Zhixing Yu, Jing Cui, Zongmin Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

The development and applications of artificial intelligence (AI) have brought unprecedented opportunities to humans, but also brought many challenges and concerns such as unfairness, immorality, distrust, illegality, and discrimination. Responsible AI provides a new solution to effectively address these AI potential threats by integrating social/physical rules into AI systems. However, these rules are high-level regulations and ethical principles, which are difficult to be formalized. To this end, we attempt to use the data generated in various AI systems such as cyber-physical-social systems (CPSS) to discover and reflect these rules to provide more responsible services for humans. In this article, we first propose a data-driven responsible CPSS framework. Its core idea is to mine valuable rules through perception, fusion, processing, and analysis of CPSS data, and then use these rules to adaptively optimize CPSS. Based on this framework, three tensor-based couple hidden Markov models (T-CHMMs) are constructed to integrate three responsible features (i.e., timing, periodicity, and correlation) for mining potential and valuable rules. Then, the corresponding tensor-based Baum-Welch (TBW) algorithms are designed to solve their learning problems. Finally, the predictive accuracy and computational efficiency of the proposed models and algorithms are verified on three open datasets. The experimental results show that proposed methods have the best performances for various scenarios, which reflects that our methods are more promising and responsible than existing methods.

关键词： Hidden Markov models prediction algorithms Tensors Artificial intelligence Data models Analytical models Algebra Activity prediction Baum-Welch algorithm learning problem of tensor-based couple hidden Markov model (T-CHMM) responsible AI tensor algebra

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Sustainable Farming: Crop prediction and Detection of Crop Deficiency using Various algorithms 4

Sustainable Farming: Crop Prediction and Detection of Crop D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Unnathi, C.S. Kalpana Devi, S. Rv University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Selection of crop suitable to the environment and soil of the farm land is very crucial for sustainable agriculture. According to The CropLife India and Yes Bank report India faces an annual crop yield loss of approximately INR 2 lakh crores due to various factors, including not proper usage of fertilizer use and unsuitable crop choices for specific soil and climate conditions. Traditional farming methods often depends on intuition and experience, leading to inefficient resource utilization and reduced crop yields. The existing systems are particularly designed for large farms which leaves small scale farmers in despair. So, we have developed a model that predicts the best suited crop for that particular land by considering N, P, K levels, humidity, rainfall, temperature and pH levels of the soil and experimented with various algorithms such as Naïve's Bayes algorithm, Random Forest, Decision tree, SVM, Logistic Regression, Linear Regression, k-NN out of which Naïve's Bayes achieved highest accuracy of 99.54%. The proposed model not only gives the best suited crop but also tells what nutrients are deficient and need to be improved for better yield recommended by official government website of agriculture domain thereby contributing to both agricultural productivity and sustainability. This work underscores the potential of smart systems to support sustainable farming by enabling precise, informed decision-making that optimizes resource use and minimizes environmental impact. © 2024 IEEE.

关键词： Training Support vector machines Soil Predictive models prediction algorithms Agriculture Bayes methods Sustainable development Crop yield Farming

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Humans Psychological Traits prediction from their Spending Patterns using Machine Learning algorithms 2

Humans Psychological Traits Prediction from their Spending P...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arpitha, C.N. Sunitha, M.R. Department of Information Science and Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India Visvesvaraya Technological University Belagavi590018 India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Analyzing human behavioral data produced by new technology has gained popularity in recent years. Spending behavior is one kind of digital footprint that is ubiquitous worldwide but has gotten very little attention up to this point. In this research, the analysis of the extent to which psychological traits at the individual level may be deduced from bank transaction data, utilizing the transaction records of over 100 bank customers is performerd. Using (i) overall spending behavior (i.e., total number and total amount of transaction), (ii) category-related spending behavior (i.e., variety, persistence, and turnover), more extensive feature space is considered and these features are used to investigate how they relate to certain psychological traits, examine how well each feature family performs, and then attempt to determine how much psychological information can be deduced from expenditure records. Most popular supervised algorithms like, Random Forest, Support vector machine (SVM), Decision Tree and Logistic regression algorithms are used for classifying the humans different personality traits and their performance analysis are represented. And the experimantal results claims that better accuracy is attained by Random Forest and Decision tree approaches for categorization. © 2024 IEEE.

关键词： Support vector machines Logistic regression Privacy Machine learning algorithms Psychology prediction algorithms Classification algorithms Performance analysis Decision trees Random forests

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A Water Quality prediction Model Based on Long Short-Term Memory Networks and Optimization algorithms

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IEEE ACCESS 2024年 12卷 175607-175615页

作者： Yu, Aihua Xiao, Qingjie Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Zhejiang Peoples R China

Currently, the water environment is complicated, and the water quality factors are non-linear and non-stationary, so the accuracy of traditional water quality prediction model is restricted. In order to improve the model performance and the prediction accuracy of the water quality factors (PH, dissolved oxygen, ammonia nitrogen and total phosphorus), a prediction model based on AWPSO-LSTMAT is proposed in this paper. The Zhou River in Haihe River Basin will be the major research objective of this study, therefore, the monitoring data of four types of water quality factors collected from the Xitunqiao water quality monitoring section is considered as the data set. In comparison with the previous prediction models such as SVR, LSTM, CNN-LSTM and CNN-GRU, obviously, the prediction effect of AWPSO-LSTMAT is significantly improved by means of modifying and optimizing the original algorithm. The mean absolute error (MAE) in predicting PH, dissolved oxygen (DO), ammonia nitrogen (AN) and total phosphorus (TP) are 0.039, 0.34, 0.119 and 0.019, respectively. Moreover, the designed prediction model can own higher prediction accuracy and stronger generalization than other existed models. According to above-mentioned illustration, this study can provide reliable technical support for the early warning of water quality, and has a good application value for the water environment treatment in the Zhou River.

关键词： Rivers Water quality Logic gates Predictive models Long short term memory Feature extraction Accuracy Vectors Tuning prediction algorithms Water quality prediction water quality factors long short-term memory network attention mechanism adaptive weight particle swarm optimization

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A Feature-Aware Approach to Acupoint Compatibility prediction Using Residual Graph Attention Networks and Matrix Factorization

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第5期29卷 3750-3761页

作者： Li, Ruiling Pan, Ying Wu, Song Ma, Li Peng, Limei Hubei Univ Chinese Med Sch Informat Engn Wuhan 430065 Peoples R China Hubei Univ Chinese Med Sch Acupuncture Moxibust & Orthoped Wuhan 430065 Peoples R China Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea

Compatibility among acupoints is a fundamental principle in acupuncture treatment within traditional Chinese medicine, playing a vital role in enhancing the effectiveness and scope of therapeutic interventions. With the increasing availability of acupuncture-related data, link prediction offers a data-driven approach that facilitates the evidence-based exploration and validation of acupoint compatibilities. However, existing link prediction methods often focus on mapping acupoints and their compatibility relationships into lower-dimensional spaces. These approaches can overlook essential acupoint features and make the predictions susceptible to noise interference. To address these challenges, we propose a novel acupoint compatibility prediction model based on a Feature-Aware Residual Graph Attention Network and Matrix Factorization (FRGATMF). Our model introduces a feature-aware connectivity fusion strategy that integrates acupoint attributes with structural information to enrich acupoint representations. Following this, a deep non-negative matrix factorization approach is employed to construct a denoised feature matrix. This matrix is processed through a residual graph attention network to derive comprehensive and effective node embeddings, which are crucial for accurate link prediction. Experimental results on the acupuncture dataset, along with three public datasets, demonstrate that FRGATMF significantly outperforms seven existing comparison models across various evaluation metrics. Additionally, link prediction can identify previously unconsidered or undocumented acupoint combinations that may offer better therapeutic results, thus expanding the range of treatment options and highlighting its potential in improving the prediction of acupoint compatibility relationships.

关键词： Predictive models Acupuncture Noise Drugs Autoencoders Adaptation models Semantics prediction algorithms Diseases Biological system modeling Acupoint compatibility deep non-negative matrix factorization residual graph attention network link prediction

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Radar Aided 6G Beam prediction: Deep Learning algorithms and Real-World Demonstration

Radar Aided 6G Beam Prediction: Deep Learning Algorithms and...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Demirhan, Umut Alkhateeb, Ahmed Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781665442664

Adjusting the narrow beams at millimeter wave (mmWave) and terahertz (THz) MIMO communication systems is associated with high beam training overhead, which makes it hard for these systems to support highly-mobile applications. This overhead can potentially be reduced or eliminated if sufficient awareness about the transmitter/receiver locations and the surrounding environment is available. In this paper, efficient deep learning solutions that leverage radar sensory data are developed to guide the mmWave beam prediction and significantly reduce the beam training overhead. Our solutions integrate radar signal processing approaches to extract the relevant features for the learning models, and hence optimize their complexity and inference time. The proposed machine learning based radar-aided beam prediction solutions are evaluated using a large-scale real-world mmWave radar/communication dataset and their capabilities were demonstrated in a realistic vehicular communication scenario. In addition to completely eliminating the radar/communication calibration overhead, the proposed algorithms are able to achieve around 90% top-5 beam prediction accuracy while saving 93% of the beam training overhead. This highlights a promising direction for addressing the training overhead challenge in mmWave/THz communication systems.

关键词： Training Deep learning Machine learning algorithms Signal processing algorithms Millimeter wave radar prediction algorithms Feature extraction

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Efficient prediction of Cardiovascular Disease Using Machine Learning algorithms With Relief and LASSO Feature Selection Techniques

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IEEE ACCESS 2021年 9卷 19304-19326页

作者： Ghosh, Pronab Azam, Sami Jonkman, Mirjam Karim, Asif Shamrat, F. M. Javed Mehedi Ignatious, Eva Shultana, Shahana Beeravolu, Abhijith Reddy De Boer, Friso Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1225 Bangladesh Charles Darwin Univ Coll Engn IT & Environm Casuarina NT 0810 Australia Govt Bangladesh Minist Posts Telecommun & Informat Technol Informat & Commun Technol Div Dhaka 1000 Bangladesh

Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

关键词： Heart Predictive models prediction algorithms Boosting Support vector machines Feature extraction Classification algorithms Heart disease machine learning CVD relief feature selection LASSO feature selection decision tree random forest K-nearest neighbors AdaBoost and gradient boosting

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An Empirical Analysis of Machine Learning algorithms for Crime prediction Using Stacked Generalization: An Ensemble Approach

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IEEE ACCESS 2021年 9卷 67488-67500页

作者： Kshatri, Sapna Singh Singh, Deepak Narain, Bhavana Bhatia, Surbhi Quasim, Mohammad Tabrez Sinha, G. R. MATS Univ Dept Comp Sci & Applicat Raipur 492001 Madhya Pradesh India Bennett Univ Dept Comp Sci & Engn Noida 201310 India King Faisal Univ Dept Informat Syst Coll Comp Sci & Informat Technol Al Hasa 36362 Saudi Arabia Univ Bisha Coll Comp & Informat Technol Bisha 67714 Saudi Arabia Myanmar Inst Informat Technol MIIT Dept Elect & Commun Engn Mandalay 05053 Myanmar

Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naive byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.

关键词： Predictive models Data models Machine learning prediction algorithms Machine learning algorithms Artificial neural networks Support vector machines Boosting classifier ensemble classifiers Indian crime prediction machine learning statistical classifiers stacking

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Development of an Extreme Gradient Boosting Model Integrated With Evolutionary algorithms for Hourly Water Level prediction

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IEEE ACCESS 2021年 9卷 125853-125867页

作者： Nguyen, Duc Hai Hien Le, Xuan Heo, Jae-Yeong Bae, Deg-Hyo Sejong Univ Dept Civil & Environm Engn Seoul 143747 South Korea Thuyloi Univ Fac Water Resources Engn Hanoi 116705 Vietnam

The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

关键词： Predictive models Radio frequency Floods Machine learning algorithms Genetic algorithms Urban areas prediction algorithms Extreme gradient boosting evolutionary algorithms water level prediction tree-based model urban floods

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