Using neural networks and SVMs for automatic medical diagnosis: A comprehensive review

Vassis, Dimitris; Kampouraki, B. A.; Belsis, Petros; Zafeiris, V.; Vassilas, Nikolaos; Galiotou, Eleni; Karanikolas, Nikitas N.; Fragos, Kostas; Kaburlasos, Vassilis G.; Papadakis, Stelios; Tsoukalas, Vassilis; Skourlas, Christos

doi:10.1063/1.4907814

Cited by 9 publications

(3 citation statements)

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“…Vidyullatha .P et al [32] gave an idea about decision making for the heart patient. For diagnosing the heart disease, the right decision and treatment recommended by the physicians is so important so far.…”

Section: Related Workmentioning

confidence: 99%

Literature Survey on Machine Learning Based Techniques in Medical Dataanalysis

Vemulapalli

Sekhar²

2019

IJAR

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Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades

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Section: Related Workmentioning

confidence: 99%

Literature Survey on Machine Learning Based Techniques in Medical Dataanalysis

Vemulapalli

Sekhar²

2019

IJAR

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“…Classification problem with class imbalance where one class is rare compared to the other is a common yet important problem in supervised learning. It arises in many applications, ranging from medical diagnosis and text retrieval to credit risk prediction and fraud detection [1][2][3][4]. Due to its practical importance, it has been identified as one of the ten most challenging problems in data mining research [5].…”

Section: Introductionmentioning

confidence: 99%

A Structural SVM Based Approach for Binary Classification under Class Imbalance

Cheng

Yang

Zhang

2015

Mathematical Problems in Engineering

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Class imbalance situations, where one class is rare compared to the other, arise frequently in machine learning applications. It is well known that the usual misclassification error is not suitable in such settings. A wide range of performance measures such as AM and QM have been proposed for this problem. However, due to computational difficulties, few learning techniques have been developed to directly optimize for AM or QM metric. To fill the gap, in this paper, we present a general structural SVM framework for directly optimizing AM and QM. We define the loss functions oriented to AM and QM, respectively, and adopt the cutting plane algorithm to solve the outer optimization. For the inner problem of finding the most violated constraint, we propose two efficient algorithms for the AM and QM problem. Empirical studies on the various imbalanced datasets justify the effectiveness of the proposed approach.

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“…A decision tree [4][5] is a classic algorithm in the medical classification domain, one that uses the information entropy method; however, it is sensitive to inconsistencies in the data. The support vector machine [6][7][8] has a solid theoretical basis for the classification task; because of its efficient selection of features, it has higher predictive accuracy than decision trees. Bayesian networks [9][10], which are based on Bayesian theory [11][12], describe the dependence relationship between the symptom variables and the disease variables; these can be used in medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Learning and inference in knowledge-based probabilistic model for medical diagnosis

Jiang

Li²,

Zhao

et al. 2017

Knowledge-Based Systems

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Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge network (MKN) in medical diagnosis. When a set of symptoms is activated for a specific patient, we can generate a ground medical knowledge network composed of symptom nodes and potential disease nodes. By Incorporating a Boltzmann machine into the potential functio n of a Markov network, we investigated the joint probability distribution of the MKN. In order to deal with numerical symptoms, a multivariate inference model is presented that uses conditional probability. In addition, the weights for the knowledge graph were efficie ntly learned from manually annotated Chinese Electronic Medical Records (CEMRs). In our experiments, we found numerically that the optimum choice of the quality of disease node and the expression of symptom variable can improve the effectiveness of medical diagnosis. Our experimental results comparing a Markov logic network and the logistic regression algorithm on an actual CEMR database indicate that our method holds promise and that MKN can facilitate studies of intelligent diagnosis.

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Using neural networks and SVMs for automatic medical diagnosis: A comprehensive review

Cited by 9 publications

References 0 publications

Literature Survey on Machine Learning Based Techniques in Medical Dataanalysis

Literature Survey on Machine Learning Based Techniques in Medical Dataanalysis

A Structural SVM Based Approach for Binary Classification under Class Imbalance

Learning and inference in knowledge-based probabilistic model for medical diagnosis

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