A review and comparison of classification algorithms for medical decision making

Harper, Paul Robert

doi:10.1016/j.healthpol.2004.05.002

Cited by 140 publications

(103 citation statements)

References 14 publications

(15 reference statements)

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Recently researchers uses data mining tools in distributed medical environment in order to provide better medical services to a large proportion of population at a very low cost, better customer relationship management, better management of healthcare resources, etc. It provides meaningful information in the field of healthcare which may be then useful for management to take decisions such as estimation of medical staff, decision regarding health insurance policy, selection of treatments, disease prediction etc., [6][7][8][9]. Dealing with the issues and challenges of data mining in healthcare [10,11].…”

Section: Figure 1: Stages Of Knowledge Discovery Processmentioning

confidence: 99%

Methodologies of Data Mining in Healthcare: A Inspection

Periasamy¹,

Mohan²

2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

show abstract

Section: Figure 1: Stages Of Knowledge Discovery Processmentioning

confidence: 99%

Methodologies of Data Mining in Healthcare: A Inspection

Periasamy¹,

Mohan²

2017

IJARCSSE

View full text Add to dashboard Cite

show abstract

“…The Back Propagation Algorithm is a multi-layered Neural Networks for learning rules [4], credited to Rumelhart and McClelland. It produces a prescription for adjusting the initially randomized set of synaptic weights such that to maximize the difference between the neural network's output of each input fact and the output with which the given input is known (or desired) to be associated.…”

Section: Back Propagation Algorithmmentioning

confidence: 99%

“…The back propagation algorithm uses a computed output error to change the weight values in backward direction [12]. To get this net error, a forward propagation phase must have been done before.…”

Section: Back Propagation Algorithmmentioning

confidence: 99%

“…Lung-Cheng Huang reported that Naïve Bayesian classifier produces high performance than SVM and C 4.5 for the CDC Chronic fatigue syndrome dataset [14]. Paul R Harper [12] reported that there is not necessary a single best classification tool but instead the best performing algorithm will depend on the features of the dataset to be analyzed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis

Ramana¹,

Babu²,

Venkateswarlu³

2011

IJDMS

177

View full text Add to dashboard Cite

show abstract

“…The most common classification models used in such cases include: logistic regression, a decision tree, and the artificial neural network. The last two methods are considered to be among the non-parametric methods of data mining, and these methods differ from each other in terms of the classification accuracy, interpretability of the results, calculation time, and availability of statistical software applications, irrespective of the differences in the estimation methods and calculation algorithms as mentioned in many studies (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

Predicting Unwanted Pregnancies among Multiparous Mothers in Khorramabad, Iran

Ebrahimzadeh

Azarbar

Almasian

et al. 2016

Iran Red Crescent Med J

View full text Add to dashboard Cite

Background: Unwanted pregnancy is the kind of pregnancy which is undesirable for at least one of the parents, and is accompanied by unfavorable consequences for the family and society. Objectives: In this study, three classification models have been used to predict the occurrence of unwanted pregnancies in the urban population in Khorramabad, Iran, and the performance of these models was compared. Methods: In this cross-sectional study, 467 multiparous mothers referred to the health centers of Khorramabad in 2012 were selected using a combination of cluster and stratified sampling, and the relevant variables were measured. The logistic regression, decision tree, and a neural network were implemented using SPSS version 21 and MATLAB version R2013a. To compare these models, the indices of sensitivity and specificity, the area under the ROC curve, and the correct percentage of the predictions were used. Results: Overall, the prevalence of unwanted pregnancies was 32.3%. The performance of the models based on the area under the ROC curve as the indicator was as follows: artificial neural networks (0.741), decision tree (0.731), and logistic regression (0.712). The highest sensitivity level belonged to the decision tree (73.5%), and the highest specificity level belonged to the artificial neural network (62.3%). Conclusions: Given the high prevalence of unwanted pregnancies in Khorramabad, Iran, it is necessary to revise and improve the family planning projects. In selecting the best classification method, if the researcher is interested in the better interpretability of the results, the use of the decision tree and logistic regression is recommended; however, if the researcher is interested in a higher prediction power of the model, the neural network is recommended.

show abstract

A review and comparison of classification algorithms for medical decision making

Cited by 140 publications

References 14 publications

Methodologies of Data Mining in Healthcare: A Inspection

Methodologies of Data Mining in Healthcare: A Inspection

A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis

Predicting Unwanted Pregnancies among Multiparous Mothers in Khorramabad, Iran

Contact Info

Product

Resources

About