Prediction Of Long Term Living Donor Kidney Graft Outcome: Comparison Between Different Machine Learning Methods

Predicting the outcome of a graft transplant with high level of accuracy is a challenging task In medical fields and Data Mining has a great role to answer the challenge. The goal of this study is to compare the performances and features of data mining technique namely Decision Tree , Rule Based Classifiers with Compare to Logistic Regression as a standard statistical data mining method to predict the outcome of kidney transplants over a 5-year horizon. The dataset was compiled from the Urology and Nephrology Center (UNC), Mansoura, Egypt. classifiers were developed using the Weka machine learning software workbench by applying Rule Based Classifiers (RIPPER, DTNB),Decision Tree Classifiers (BF,J48 ) and Logistic Regression. Further from Experimental Results, it has been found that Decision Tree and Rule Based classifiers are providing improved Accuracy and interpretable models compared to other Classifier.

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MINING SIMILAR PATTERN WITH ATTRIBUTE ORIENTED INDUCTION HIGH LEVEL EMERGING PATTERN (AOI-HEP) DATA MINING TECHNIQUE

Jurnal Teknologi ◽

10.11113/jt.v79.11876 ◽

2017 ◽

Vol 79 (7-2) ◽

Author(s):

Harco Leslie Hendric Spits Warnars ◽

Nizirwan Anwar ◽

Richard Randriatoamanana ◽

Horacio Emilio Perez Sanchez

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Data Mining ◽

Similar Pattern ◽

Frequent Pattern ◽

Data Mining Technique ◽

Mining Technique ◽

High Level ◽

Concept Attribute ◽

Emerging Pattern

AOI-HEP (Attribute Oriented Induction High Emerging Pattern) as new data mining technique has been success to mine frequent pattern and is extended to mine similar patterns. AOI-HEP is success to mine 3 and 1 similar patterns from IPUMS and breast cancer UCI machine learning datasets respectively. Meanwhile, the experiments showed that there was no finding similar patterns on adult and census UCI machine learning datasets. The experiments showed that finding AOI-HEP similar pattern in dataset is influenced by learning on chosen high level concept attribute in concept hierarchy and it is applied to AOI-HEP frequent pattern in previous research as well. The experiments chosed high level concept attributes such as workclass, clump thickness, means and marts for adult, breast cancer, census and IPUMS datasets respectively. In order to proof that the chosen high level concept attribute will influences the AOI-HEP similar pattern in dataset, then extended experiments were carried on and the finding were census dataset which had been none AOI-HEP similar pattern, had AOI-HEP similar pattern when learned on high level concept in marital attribute. Meanwhile, Breast cancer which had been had 1 AOI-HEP similar pattern, had none AOI-HEP similar pattern when learned on high level concept in attributes such as cell size, cell shape and bare nuclei. The 2 of 3 finding Similar patterns in IPUMS dataset have strong discriminant rule since having large growth rates such as 1.53% and 3.47%, and having large supports in target dataset such as 4.54% and 5.45 respectively. Moreover, there have small supports in contrasting dataset such as 2.96% and 1.57% respectively.

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Application of Data Mining Algorithms for Dementia in People with HIV/AIDS

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4602465 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Luana Ibiapina Cordeiro Calíope Pinheiro ◽

Maria Lúcia Duarte Pereira ◽

Marcial Porto Fernandez ◽

Francisco Mardônio Vieira Filho ◽

Wilson Jorge Correia Pinto de Abreu ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Data Mining ◽

Logistic Regression ◽

Random Forest ◽

Decision Tree ◽

Learning Algorithms ◽

Principal Component ◽

Machine Learning Algorithms ◽

Hiv Aids

Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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Mining Association rule with Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) data mining technique

2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) ◽

10.1109/icicct.2018.8472955 ◽

2018 ◽

Author(s):

Harco Leslie Hendric Spits Warnars ◽

Ford Lumban Gaol ◽

Yaya Heryadi ◽

Agung Trisetyarso ◽

Horacio Emilio Perez Sanchez

Keyword(s):

Data Mining ◽

Association Rule ◽

Data Mining Technique ◽

Mining Technique ◽

High Level ◽

Mining Association Rule ◽

Emerging Pattern

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Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease

Frontiers in Health Informatics ◽

10.30699/fhi.v10i1.349 ◽

2021 ◽

Vol 10 (1) ◽

pp. 99

Author(s):

Sajad Yousefi

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Heart Disease ◽

Decision Tree ◽

Roc Curve ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Learning Models ◽

Algorithm Performance ◽

Machine Learning Models

Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.

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Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study (Preprint)

10.2196/preprints.17110 ◽

2019 ◽

Author(s):

Cheng-Sheng Yu ◽

Yu-Jiun Lin ◽

Chang-Hsien Lin ◽

Sen-Te Wang ◽

Shiyng-Yu Lin ◽

...

Keyword(s):

Machine Learning ◽

Metabolic Syndrome ◽

Logistic Regression ◽

Decision Tree ◽

Characteristic Curve ◽

Significant Risk ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Health Examination ◽

Multivariate Logistic Regression

BACKGROUND Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

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Factors Influencing Secondary School Student’s Performance Through Variable Decision Tree Data Mining Technique

International Journal of Data Science and Analysis ◽

10.11648/j.ijdsa.20200605.11 ◽

2020 ◽

Vol 6 (5) ◽

pp. 120

Author(s):

Yousaf Ali Khan

Keyword(s):

Data Mining ◽

Secondary School ◽

Decision Tree ◽

Data Mining Technique ◽

Mining Technique ◽

Factors Influencing ◽

Student’S Performance ◽

Tree Data

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A Survey on Building Recommendation Systems Using Data Mining Techniques

10.4018/978-1-7998-8413-2.ch002 ◽

2022 ◽

pp. 24-56

Author(s):

Rajab Ssemwogerere ◽

Wamwoyo Faruk ◽

Nambobi Mutwalibi

Keyword(s):

Machine Learning ◽

Data Mining ◽

Recommender Systems ◽

Performance Measures ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Learning Hypothesis ◽

Depth Study ◽

And Performance ◽

Using Data

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

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