HMV: A medical decision support framework using multi-layer classifiers for disease prediction

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan; Naseem, Lubna

doi:10.1016/j.jocs.2016.01.001

Cited by 67 publications

(36 citation statements)

References 28 publications

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“…In former studies, it has been demonstrated that one single FS method cannot perform well with all classifiers and datasets. 26 The ensemble FS (EFS) method is therefore an effective approach to provide the optimal FS method. The EFS method is an approach that leads to higher stability and a better predictive model by aggregating many weaker prediction models.…”

Section: Phase-2: Ensemble Feature Selectionmentioning

confidence: 99%

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A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Tripathi

Edla

Cheruku

et al. 2019

Computational Intelligence

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Credit scoring focuses on the development of empirical models to support the financial decision-making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real-world credit scoring datasets, namely, Australian, Japanese, German-categorical, and German-numerical datasets.

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Section: Phase-2: Ensemble Feature Selectionmentioning

confidence: 99%

“…However, no classifier can perform well for all datasets. 26 However, when a multilayer ensemble classifier is used, the placement of each classifier may affect the overall performance. As a consequence, using an ensemble classifier is a strong approach to get near to the optimal classifier for any dataset.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Tripathi

Edla

Cheruku

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

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“…S. Bashir [19] presented heterogeneous classifier namely HMV for medical data classification. This classifier resolved the storage problem by selecting the important features for disease analysis, but it is difficult to perform in imbalanced medical datasets.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, the Table 2 represents the comparative study of existing and the proposed GDLR based medical disease classification. The Cleveland datasets are used to measure the performance of the proposed GDLR and compared with other existing method [18,19] in same dataset. The proposed GDLR method has the higher performance due to feature are selected by random forest are further analyzed by the GDLR to measure the feature weight value and classify the medical data based on the feature weight values.…”

Section: Quantitative Analysismentioning

confidence: 99%

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Kumar¹,

Gera²,

Kamakshi³

2019

IJIES

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In recent years, a number of researchers have concentrated on medical data analytics because machine intelligence in medical diagnosis is a new trend for enormous medical applications. Generally, medical datasets are massive in size, so traditional classifiers suffered from overfitting and under-fitting problem of training set. In this paper, Gradient Descent Logistic Regression (GDLR) classification method is proposed for medical data classification. The Pearson Correlation Coefficient (PCC) is used to calculate the correlation between the features. After that, Random Forest (RF) algorithm ranks the features and selects the most relevant features to improve performance of the medical data classification. The regression technique processes the features effective and analyse the feature importance based on the weight values. The Random Forest (RF) assigns the features importance in the tree structure. The random forest is used to select the features and features are applied for the GDLR to classify effectively. The GDLR method further analysis the features for effectively analysis the feature importance based on the weight values and more relevant features are identified than the RF. The experimental analysis demonstrated that the performance of GDLR algorithm achieved better than traditional methods Neural Network for Threshold Selection (NNTS) and Mean Selection (MS). The accuracy of the proposed GDLR method achieved as 97.5% in the Hepatitis dataset, while existing mean selection method has the accuracy of 82.58%.

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“…Bashir et al proposed a multi‐layer classifier ensemble structure based on the optimal blend of heterogeneous classifiers, which is termed as “HMV (Hierarchical Majority Voting),” which overcomes the drawbacks of conventional performance bottlenecks by using an ensemble of seven heterogeneous classifiers, namely, Quadratic Discriminant Analysis, Support Vector Machine, Decision tree using Gini Index, Naïve Bayes, Linear Regression, Decision tree using Information Gain, and K‐Nearest neighbor. The dataset is collected from both heart disease dataset as well as real time dataset from PIMS hospital.…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering‐based approach for medical data classification

Kodabagi

Tikotikar

2018

Concurrency and Computation

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Summary Medical data records are growing enormously in present days due to the growth of various medical technologies and population of the globe. It is a difficult task for a medical expert to process these data records to identify and provide treatment regarding the disease of patient. Hence, it is necessary to automate the processing of such medical data using machine learning‐based Medical Decision Support System. In this proposed work, an Effective Fuzzy Rule Classifier (EFRC)–based decision support system is used for analysis of UCI medical dataset for identification of disease of patients. Initially, the method determines the best centroid value for each of the attribute using training dataset. Furthermore, the centroid value is refined using test samples. The refined centroid values of all the attributes are used by the fuzzy classifier for medical data classification. Experimental results have proven that EFRC performs better classification than existing systems in terms of accuracy, sensitivity, and specificity.

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HMV: A medical decision support framework using multi-layer classifiers for disease prediction

Cited by 67 publications

References 28 publications

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Clustering‐based approach for medical data classification

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