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Comparison of Different classification algorithms for Prediction of Heart Disease by Machine Learning Techniques

SN Computer Science 2023年第2期4卷 1-5页

作者： Harshitha, B. Maria Rufina, P. Shilpa, B.L. GSSSIETW Mysuru India

Cardiovascular disease commonly referred as heart disease, encompasses diverse conditions that the heart undergoes which in turn leads to sudden death or prolonged sickness worldwide over the past decades. More recently, foreseeing heart disease is the stimulating responsibility in the health arena. In recent eras, every minute approximately one person expires due to heart ailment. Data Science processes big volumes of healthcare data and researchers apply a variety of datamining and machine learning techniques to analyze vast and complex medical data to help health care professionals predict heart disease. This tabloid collects heart disease dataset from UCI machine learning source analyzing which envisages the accuracy of heart disease by considering major risk factors based on different classifier algorithms. This research paper objective is to diagnose imminent heart disease via scrutinizing data of patients and analyzing if heart disease is pestilent with machine-learning algorithm. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： classification algorithm Heart disease prediction Logistic regression Random Forest

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AN algorithm FOR THE classification OF STRONGLY REGULAR GRAPHS BY MEANS OF LEXICALLY ORDERED ADJACENCY MATRICES

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 1990年第3-4期33卷 143-151页

作者： FACK, V COOLSAET, K STATE UNIV GHENT SEMINARIE ALGEBRA & FUNCT ANALB-9000 GHENTBELGIUM

The concept of lexically ordered adjacency matrix of a graph is introduced and it is proved that every adjacency matrix is isomorphic to at least one lexically ordered adjacency matrix. An algorithm for the classification of strongly regular graphs is developed, where the property of lexical ordering is used as a means to reduce the number of generated adjacency matrices. We also describe other pruning methods that can be used. © 1990, Taylor & Francis Group, LLC. All rights reserved.

关键词： adjacency matrix classification algorithm lexical ordering Strongly regular graph

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Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期237卷

作者： Zhang, Xiaoming Yu, Lean Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330032 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China Shenzhen Inst Technol Sch Business Shenzhen 518116 Peoples R China

Credit risk assessment is a crucial element in credit risk management. With the extensive research on consumer credit risk assessment in recent decades, the abundance of literature on this topic can be overwhelming for researchers. Therefore, this article aims to provide a more systematic and comprehensive analysis from three perspectives: classification algorithms, data traits, and learning methods. Firstly, the state-of-the-art classification algorithms are categorized into traditional single classifiers, intelligent single classifiers, hybrid and ensemble multiple classifiers. Secondly, considering the diversity of data traits in the credit dataset, data traits are divided into external structure information traits, data quality traits, data quantity traits, and internal information traits. Data traits-driven modeling framework based on multiple classifiers is proposed for solving credit risk assessment. Thirdly, considering the differences in data modeling methods, learning methods are classified into data status, label status, and structure form. Furthermore, model interpretability, model bias, model multi-pattern, and model fairness are discussed. Finally, the limitations and future research directions are presented. This review article serves as a helpful guide for researchers and practitioners in the field of credit risk modeling and analysis.

关键词： Credit risk assessment classification algorithm Data trait Learning method

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Discrimination of citrus infested with Bactrocera dorsalis (Hendel) using an olfactory detection system based on MIPs-QCM sensor array and Bayesian optimized classification algorithms

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SENSORS AND ACTUATORS B-CHEMICAL 2023年 385卷

作者： Wen, Tao He, Weitao Nie, Qiyi Gong, Zhongliang Li, Dapeng Wen, Liang Peng, Hailong Cent South Univ Forestry & Technol Sch Mech & Elect Engn Changsha 410004 Hunan Peoples R China Nanchang Univ Sch Chem & Chem Engn Nanchang 330031 Jiangxi Peoples R China

The characteristic substances in citrus volatile organic compounds (VOCs) are associated with the infestation of the Bactrocera dorsalis (Hendel), which provides a noninvasive evaluation method to discriminate infested citrus. This paper developed an olfactory detection system based on a quartz crystal microbalance (QCM) sensor array and classification algorithms to identify the citrus infested with B. dorsalis. Six characteristic substances, including D-limonene, myrcene, alpha-pinene, decanal, linalool, and beta-ocimene, which vary significantly after B. dorsalis infestation were selected as template molecules to prepare molecularly imprinted polymers (MIPs) and modify QCM respectively. The experimental results show that the prepared MIPs-QCM sensors had sensitivity in the range of 0.043-0.070 Hz/(mg/m3), and their stability and reproducibility were above 93.1%. Four sensors that contributed to the classification were screened by a stepwise discriminant analysis. Afterward, Bayesian optimization was employed to optimize the hyperparameters. The accuracy of the optimized support vector machine (SVM) reached 94.17%. The olfactory detection system developed in this study enables the discrimi-nation of citrus infested with B. dorsalis, which may have potential applications in the field of post-harvest treatment of citrus.

关键词： Bactrocera dorsalis-infested citrus Molecularly imprinted polymers Quartz crystal microbalance Volatile organic compounds classification algorithm Bayesian optimization

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A Survey on the classification algorithms based on Big Data

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第5期7卷 222-228页

作者： Xudong Wei Zhaofei Li Zenan Wang Yumei Chen Xiaodong Tong Sijing Deng Wei Pu Yuanbo Zhang Xueshen Liu Li He Bin Liu

With the explosive growth of data, the use of big data technology machine learning classification algorithms to predict the results can improve the intelligent classification of data. It can provide data support for predicting classification in advance. Filter out the classification results to improve the efficiency of data processing and data realization. This article first introduces the development process of machine learning under big data, introduces the mainstream distributed processing framework spark, and then compares the advantages and disadvantages of classification algorithms under big data.

关键词： Big Data Spark Machine Learning classification algorithm

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On the Generalizability of Machine Learning classification algorithms and Their Application to the Framingham Heart Study

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INFORMATION 2024年第5期15卷 252-252页

作者： Kahouadji, Nabil NE Illinois Univ Coll Arts & Sci Dept Math 5500 N St Louis Ave Chicago IL 60625 USA

The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and be compounded during problem selection, data collection, and outcome definition, this research pertains to the generalizability impediments that occur during the development and post-deployment of machine learning classification algorithms. Using the Framingham coronary heart disease data as a case study, we show how to effectively select a probability cutoff to convert a regression model for a dichotomous variable into a classifier. We then compare the sampling distribution of the predictive performance of eight machine learning classification algorithms under four stratified training/testing scenarios to test their generalizability and their potential to perpetuate biases. We show that both extreme gradient boosting and support vector machine are flawed when trained on an unbalanced dataset. We then show that the double discriminant scoring of type 1 and 2 is the most generalizable with respect to the true positive and negative rates, respectively, as it consistently outperforms the other classification algorithms, regardless of the training/testing scenario. Finally, we introduce a methodology to extract an optimal variable hierarchy for a classification algorithm and illustrate it on the overall, male and female Framingham coronary heart disease data.

关键词： machine learning classification algorithm health disparities variable selection methodology optimal variable hierarchy

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An Experimental Comparative Study on Three classification algorithms

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Journal of Shanghai Jiaotong university(Science) 2003年第2期8卷 133-136页

作者：蔡巍王永成李伟尹中航 Dept. of Computer Science & Eng. Shanghai Jiaotong Univ. Shanghai 200030 China

classification algorithm is one of the key techniques to affect text automatic classification system’s performance, play an important role in automatic classification research area. This paper comparatively analyzed k-NN. VSM and hybrid classification algorithm presented by our research group. Some 2000 pieces of Internet news provided by ChinaInfoBank are used in the experiment. The result shows that the hybrid algorithm’s performance presented by the groups is superior to the other two algorithms.

关键词： classification algorithm k-NN VSM hybrid algorithm

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A Critical Study of classification algorithms Using Diabetes Diagnosis

A Critical Study of Classification Algorithms Using Diabetes...

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IEEE International Advance Computing Conference

作者： Panigrahi Srikanth Dharmaiah Deverapalli Department of Information Technology VNR VJIET Department of Information Technology Shri Vishnu Engineering College for Woman

ISBN: (纸本)9781467382878

This paper predicts the Diabetes Disease based on Data Mining Techniques of classification algorithms. classification algorithm and tools may reduce heavy work on Doctors. In this paper Evaluated as classification algorithms for the Classify of some Diabetes Disease Patient Datasets. Data Mining is one of the main algorithm is classification. classification algorithm Examine of the Decision Tree algorithm, Byes algorithm and Rule based algorithm. These algorithms are evaluate Error Rates and identify of the patients based evolution Function of the measure the accurate results.

关键词： Pima Diabetes Disease Data set classification algorithm Decision Tree algorithm Byes algorithms and Rules

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Sample Size Requirements for Popular classification algorithms in Tabular Clinical Data: Empirical Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2024年 26卷 e60231页

作者： Silvey, Scott Liu, Jinze Virginia Commonwealth Univ Sch Publ Hlth Dept Biostat 830 East Main St Richmond VA 23219 USA

Background: The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to determine an optimal sample size that maximizes performance while accounting for computational burden or budgetary concerns. Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms. Methods: A total of 16 large open-source datasets were collected, each containing a binary clinical outcome. Furthermore, 4 machine learning algorithms were assessed: XGBoost (XGB), random forest (RF), logistic regression (LR), and neural networks (NNs). For each dataset, the cross-validated area under the curve (AUC) was calculated at increasing sample sizes, and learning curves were fit. Sample sizes needed to reach the observed full-dataset AUC minus 2 points (0.02) were calculated from the fitted learning curves and compared across the datasets and algorithms. Dataset-level characteristics, minority class proportion, full-dataset AUC, number of features, type of features, and degree of nonlinearity were examined. Negative binomial regression models were used to quantify relationships between these characteristics and expected sample sizes within each algorithm. A total of 4 multivariable models were constructed, which selected the best-fitting combination of dataset-level characteristics. Results: Among the 16 datasets (full-dataset sample sizes ranging from 70,000-1,000,000), median sample sizes were 9960 (XGB), 3404 (RF), 696 (LR), and 12,298 (NN) to reach AUC stability. For all 4 algorithms, more balanced classes (multiplier: 0.93-0.96 for a 1% increase in minority class proportion) were associated with decreased sample size. Other characteristics varied in importance across algorithms-in general, more features, weaker features, and more complex relationsh

关键词： medical informatics machine learning sample size research design decision trees classification algorithm clinical research learning-curve analysis analysis analyses guidelines ML decision making algorithm curve analysis dataset

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Thermoacoustic stability prediction using classification algorithms

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DATA-CENTRIC ENGINEERING 2022年第12期3卷 e17-e17页

作者： Gaudron, Renaud Morgans, Aimee S. Imperial Coll London Dept Mech Engn London England

Predicting the occurrence of thermoacoustic instabilities is of major interest in a variety of engineering applications such as aircraft propulsion, power generation, and industrial heating. Predictive methodologies based on a physical approach have been developed in the past decades, but have a moderate-to-high computational cost when exploring a large number of designs. In this study, the stability prediction capabilities and computational cost of four wellestablished classification algorithms-the K-Nearest Neighbors, Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) algorithms-are investigated. These algorithms are trained using an in-house physicsbased low-order network model tool called OSCILOS. All four algorithms are able to predict which configurations are thermoacoustically unstable with a very high accuracy and a very low runtime. Furthermore, the frequency intervals containing unstable modes for a given configuration are also accurately predicted using multilabel classification. The RF algorithm correctly predicts the overall stability and finds all frequency intervals containing unstable modes for 99.6 and 98.3% of all configurations, respectively, which makes it the most accurate algorithm when a large number of training examples is available. For smaller training sets, the MLP algorithm becomes the most accurate algorithm. The DTalgorithm is found to be slightly less accurate, but can be trained extremely quickly and runs about a million times faster than a traditional physics-based low-order network model tool. These findings could be used to devise a new generation of combustor optimization tools that would run much faster than existing codes while retaining a similar accuracy.

关键词： classification algorithm combustion instability machine learning thermoacoustics

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