Medical Data Classification with Naive Bayes Approach

Al-Aidaroos, Khadija Mohammad; Bakar, Azuraliza Abu; Othman, Zalinda

doi:10.3923/itj.2012.1166.1174

Cited by 65 publications

(33 citation statements)

References 20 publications

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“…NB classifiers are recently used in medical domain. Al-Aidaroos et al [14] compared NB with different algorithms such as decision tree, multilayer perceptron, logistic regression, Kstar. Geenen et al [15] constructed a NB classifier for veterinary medicine.…”

Section: Introductionmentioning

confidence: 99%

An alternative evaluation of post traumatic stress disorder with machine learning methods

Omurca

Ekinci

2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

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In the world we live in, people from different professions are at increased risk for depressive symptoms and posttraumatic stress disorder (PTSD) due to hard working or extreme environmental conditions. Accurate diagnosis and determining the causes are very important to solve these kinds of psychological problems. Machine learning (ML) techniques are gaining popularity in neuroscience due to their high diagnostic capability and effective classification ability. In this paper, alternative hybrid systems which allowed us to develop automatic classifiers for finding the Posttraumatic stress disorder (PTSD) patients are proposed and compared. With the proposed system, not only the PTSD individuals are classified by ML techniques such as sequential minimal optimization (SMO), multilayer perceptron (MLP), Naïve Bayes (NB) but also the important indications of patients' trauma are determined by three popular feature selection methods such as chi-square, principal component analysis (PCA) and correlation based-feature selection (CFS). The effectiveness of the proposed system is examined on a real world dataset. Due to obtained results we can estimate the individuals as PTSD or NONPTSD patients with 74-79% accuracy range, further to that instead of 39 features 7 features are remarked as the most critical symptoms for PTSD.

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Section: Introductionmentioning

confidence: 99%

An alternative evaluation of post traumatic stress disorder with machine learning methods

Omurca

Ekinci

2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

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“…However, we obtained better results than the Naïve Bayes classifier (11.48%), which is considered as a benchmark algorithm that in any medical domain has to be tried before any other advanced method [27]. Therefore, we believe that our approach could improve performance in these medical tasks, and increase the confidence of the medical community in the use of ML approaches to support clinical decisions.…”

Section: Discussionmentioning

confidence: 94%

Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality

Mena¹,

Orozco²,

Felix³

et al. 2012

Computational and Mathematical Methods in Medicine

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Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research. In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED. We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases. Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy. The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses.

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“…These classifications were applied here because they are the most popularly used by researchers [11]. In [2], it was argued that NB has a good performance in most medical problems. It has been used widely in medical applications such as in breast cancer diagnosis, especially in classifying the accuracy of performance.…”

Section: Introductionmentioning

confidence: 99%

Analyzing performance of classifiers for medical datasets

Rosly¹,

Makhtar²,

Awang³

et al. 2018

IJET

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This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.

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Medical Data Classification with Naive Bayes Approach

Cited by 65 publications

References 20 publications

An alternative evaluation of post traumatic stress disorder with machine learning methods

An alternative evaluation of post traumatic stress disorder with machine learning methods

Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality

Analyzing performance of classifiers for medical datasets

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