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Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

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BMC MEDICAL RESEARCH METHODOLOGY 2023年第1期23卷 101-101页

作者： Hassanzadeh, Roghayyeh Farhadian, Maryam Rafieemehr, Hassan Hamadan Univ Med Sci Sch Publ Hlth Dept Biostat Hamadan Iran Hamadan Univ Med Sci Res Ctr Hlth Sci Sch Publ Hlth Dept Biostat Hamadan Iran Hamadan Univ Med Sci Sch Paramed Dept Med Lab Sci Hamadan Iran

BackgroundTrauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the ICU. The main objective of the present study is to develop and evaluate SMOTE-based machine-learning tools for predicting hospital mortality in trauma patients with imbalanced *** retrospective cohort study was conducted on 126 trauma patients admitted to an intensive care unit at Besat hospital in Hamadan Province, western Iran, from March 2020 to March 2021. Data were extracted from the medical information records of patients. According to the imbalanced property of the data, SMOTE techniques, namely SMOTE, Borderline-SMOTE1, Borderline-SMOTE2, SMOTE-NC, and SVM-SMOTE, were used for primary preprocessing. Then, the Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector machine (SVM), and Extreme Gradient Boosting (XGBoost) methods were used to predict patients' hospital mortality with traumatic injuries. The performance of the methods used was evaluated by sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), accuracy, Area Under the Curve (AUC), Geometric Mean (G-means), F1 score, and P-value of McNemar's *** the 126 patients admitted to an ICU, 117 (92.9%) survived and 9 (7.1%) died. The mean follow-up time from the date of trauma to the date of outcome was 3.98 +/- 4.65 days. The performance of ML algorithms is not good with imbalanced data, whereas the performance of SMOTE-based ML algorithms is significantly improved. The mean area under the ROC curve (AUC) of all SMOTE-based models was more than 91%. F1-score and G-means before balancing the dataset were below 70% for all ML models except ANN. In contrast, F1-score and G-means for the balanced datasets reached more than 90% for all SMOTE-based models. Amon

关键词： Imbalanced data machine learning algorithms SMOTE family techniques Traumatic injuries

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A Comparative Behavioral Analysis of ASD Kids Using machine learning algorithms

A Comparative Behavioral Analysis of ASD Kids Using Machine ...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Md. Al-Hasan Mohammed Nadir Bin Ali Ahmed Wasif Reza Mohammad Shamsul Arefin Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Tourism and HospitalityManagement Daffodil International University Dhaka Bangladesh Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects various aspects of individuals' social interactions, communication, learning, and behavior. While ASD can manifest at any stage of life, symptoms typically emerge within the initial two years. Timely identification of autism can greatly influence the effectiveness of its intervention and treatment. This study centers on identifying distinct characteristics that facilitate the automation of the ASD diagnostic process. Additionally, it aims to assess and conduct a comparative examination of different machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Logistic Regression (LR), Gradient Boosting (GB), Artificial Neural Networks (ANN), an Ensemble Model (EM), Support Vector machine (SVM), Convolutional Neural Network (CNN), and Random Forest (RF) in order to forecast the likelihood of autism disorder. Most importantly four different behavioral datasets following Autism-Spectrum Quotient (AQ-10) screening tool are normalized and merged which consisting of 3492 records, facilitates the development of robust machine learning models to predict the likelihood of ASD. Different performance metrics like; accuracy, precision, recall, F1-score, Specificity and ROC-AUC are employed to make a better comparison of the mentioned algorithms.

关键词： Support vector machines Measurement Autism machine learning algorithms Accuracy Predictive models Nearest neighbor methods Boosting Prediction algorithms Convolutional neural networks

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Utilizing Explainable AI Methodologies: LIME and SHAP, for the Classification of Natural Disasters Through machine learning algorithms

Utilizing Explainable AI Methodologies: LIME and SHAP, for t...

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Engineering and Technology (PICET), Parul International Conference on

作者： K Akshitha R Roopashree Ashwini Kodipalli Trupthi Rao Dept of AI and DS Global academy of technology Karnataka India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

This work investigates the use of machine learning for the classification of natural disasters, with a focus on the Random Forest and XG Boost algorithms with optimized hyperparameters. Explainable AI (XAI) techniques Lime and Shap provide insight into model selections, highlighting crucial characteristics such as ‘No Injured’ and ‘Total Damages’ that contribute to XG Boost's 72% accuracy. The impact of these features is graphically represented by the contribution graph. XG Boost outperforms other algorithms, demonstrating its effectiveness in classifying disasters. The study emphasizes how important XAI is for improving accuracy and offering perceptive judgments. In addition to improving model interpretability, it advances the subject of disaster classification and opens the door for other XAI applications.

关键词： Accuracy machine learning algorithms Explainable AI Disasters Predictive models Parallel processing Classification algorithms Random forests Tuning Optimization

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Malicious URL Detection: Comparative Study of machine learning algorithms

Malicious URL Detection: Comparative Study of Machine Learni...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Thadakaluru Jaswanthi Talluri Harshitha Simhadri Tanya Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Malicious URLs can be detected by leveraging lexical analysis and machine learning, where the parsed components of the URL are parsed, and its features include domain reputation, URL length, and the presence of suspicious characters. Research on the detection of malicious URLs suffers challenges in keeping pace with the changing structural landscape of URLs. Adaptation to dynamic patterns and addressing the technical issues of efficient processing and scalability become important to stay one step ahead of the evolving threat landscape. This paper concentrates on addressing these challenges with the integration of machine learning and Lexical Analysis. Features extracted in this process are then fed into machine learning algorithms for the classification of URLs and are tested on unseen data. Research performance is measured using several metrics including Precision, Recall, F1-score and Accuracy during the Model evaluation. Out of all the models experimented, Extra Trees classifier stands out the best with 92% accuracy, showing effectiveness in accurate detection of malicious URL’s.

关键词： Uniform resource locators Measurement Accuracy machine learning algorithms Computational modeling Scalability Forestry Feature extraction Data mining Random forests

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Classification and Prediction of Stroke Disease Using machine learning algorithms

Classification and Prediction of Stroke Disease Using Machin...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Podutur Lahari Akarsh Kumar D. Usha Nandini M. D. Anto Praveena Mercy Paul Selvan Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital" in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical.

关键词： Text mining machine learning algorithms Accuracy Surveillance Stroke (medical condition) Tagging Syntactics

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using machine learning algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Praddipraj N N Praveen Kumar B Sreeji S Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity.

关键词： machine learning algorithms Filtering Noise reduction machine learning Signal processing Feature extraction Data engineering

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Cutting-Edge machine learning algorithms for In-Depth Characterization and Efficiency Improvement of Phase Change Heat Transfer Mechanisms

Cutting-Edge Machine Learning Algorithms for In-Depth Charac...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Geetanjli Khambra K Praveena Manjunatha Amit Dutt Irfan Khan Ali Ashoor Issa Department of Computer Applications The Bhopal School of Social Sciences (BSSS) College Bhopal M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces a sophisticated and holistic methodology for the enhanced understanding and optimization of phase change heat transfer mechanisms. The proposed framework integrates Deep learning Neural Networks (DLNN), Random Forest (RF), Long Short-Term Memory (LSTM) Networks, Gaussian Processes (GP), and Reinforcement learning (RL) in a synergistic manner, emphasizing the iterative and adaptive nature of each algorithm. A detailed flowchart for each algorithm delineates the process, showcasing their roles in refining predictions and optimizing models. An ablation study underscores the crucial contribution of each component, revealing their collective efficacy. Comparative analyses against existing algorithms highlight the proposed method’s consistent superiority in accuracy, precision, recall, mean squared error, and Rsquared. Resource efficiency metrics further affirm its computational effectiveness. Visualizations provide a comprehensive and intuitive representation of the methodology’s strengths. The proposed framework offers a promising avenue for characterizing and optimizing phase change heat transfer, contributing to advancements in thermal sciences and real-world applications.

关键词： Radio frequency Visualization Thermal expansion machine learning algorithms Accuracy Neural networks Prediction algorithms

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Titanic Survival Predictor Using machine learning algorithms

Titanic Survival Predictor Using Machine Learning Algorithms

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Mohammed Ali Shaik P Sairam P. Rahul T. Sadhvik P. Nithin School of Computer Science & Artificial Intelligence SR University Warangal Telangana State India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Presumably, one of the most notorious and catastrophic catastrophes in history is the sinking of the RMS Titanic. Unfortunately, the Titanic sank during her inaugural voyage on April 15, 1912, early in the morning after slamming into an iceberg. Out of 2224 passengers and crew, approximately 1502 perished. Social stratification is a situation where people within a society are separated into various ranking on a hierarchical basis. These groups, also known as social classes are ranked as a social order that is made up of a person's education level, income level Next similar models based on machine learning will be chosen to come to a conclusion whether some groups of individuals are candidates to survive the long space travel. The results of application of the distinct machine learning models were illustrated down one after the other and compared through a general viewpoint of precision. Initially, to get a better feeling of how the data is distributed and what the traits are, the datasets were first examined and prepared. Following that, we ran a variety of machine learning models including Logistic regression, decision trees, random forests, naive Bayes classifier and k nearest neighbors (kNN) for prediction. While conducting our research, we benchmarked the performance of each algorithm using strict assessment techniques like evaluation of variables like the required computational effort, accuracy, precision, recall, and F1 score.

关键词： Logistic regression Technological innovation Accuracy machine learning algorithms Protocols Predictive models Prediction algorithms Space exploration Naive Bayes methods Random forests

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Comparative Analysis of machine learning algorithms for Anomaly Detection

Comparative Analysis of Machine Learning Algorithms for Anom...

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International Conference for Convergence of Technology (I2CT)

作者： Siddhesh T.S. Shinu M. Rajagopal Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the rapidly evolving digital landscape network security and efficiency are important especially with the increasingly widespread network anomalies that can cause severe performance degradation. Using dataset, Data preprocessing is performed. which includes removing missing values and standardizing attributes. A comparative analysis is been done among the models to determine the finest among them. The algorithm taken for comparing is Isolation Forest and it is compared with Random Forest, Decision Tree, K-mean and Logistic Regression. And the metrics for the comparison are accuracy, precision, recall, F1 score, training time and testing time.

关键词： Training Measurement Degradation Logistic regression machine learning algorithms Forestry Network security

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A Comparative Analysis: Breast Cancer Prediction using machine learning algorithms

A Comparative Analysis: Breast Cancer Prediction using Machi...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Srikanth Yalabaka Vanthadpula Harshini Ch.Rajendra Prasad Vipul Keerthi Janagani Avinash Kasanaboina Muneeshwar Dept. of ECE SR University Warangal India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

The majority of women are affected by breast cancer. Considering that the majority of them are unaware that they have breast cancer. Improving breast cancer survival rates requires early detection and treatment. Statistical models, expert knowledge and judgment, modelling and simulation, historical comparisons and analogies, and expert knowledge and judgment can all be used to forecast breast cancer. Identifying the drawbacks and limitations of non-ML predictions; developing artistic or literary interpretations of predictions; and developing hybrid approaches that combine various prediction techniques, human judgment, creative thinking, and other non-quantitative factors in making predictions are some of its limitations. Using models for machine learning Python-based application of decision tree, random forest, logistic regression, and KNN algorithms for the prediction of breast cancer. The algorithms obtain good accuracy, precision, recall, and F1-score when tested on a widely used dataset on breast cancer.

关键词： Radio frequency Training machine learning algorithms Accuracy Predictive models Nearest neighbor methods Prediction algorithms

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