Innovative feature selection and classification model for heart disease prediction

Nagarajan, Senthil Murugan; Muthukumaran, V.; Murugesan, R.; Joseph, Rose Bindu; Munirathanam, Meram; Prathik, A.

doi:10.1007/s40860-021-00152-3

Cited by 48 publications

(22 citation statements)

References 24 publications

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“…This work employed machine learning algorithms to classify DR cases among T2D patients and provided possible risk factors. Feature selection has been used in various domains [25,26] to improve the model prediction. The main idea of this method is to choose important features that are essential for the discovery of new knowledge [27]; a small subset of the significant features subsequently reduce the computation time.…”

Section: Discussionmentioning

confidence: 99%

Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy

Maeda-Gutiérrez

Galván-Tejada

Cruz

et al. 2021

JPM

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One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR.

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Section: Discussionmentioning

confidence: 99%

Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy

Maeda-Gutiérrez

Galván-Tejada

Cruz

et al. 2021

JPM

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“…Its fundamental concept is to approach the present candidate solution to the optimum by concurrently taking into account the evaluation of a solution and its inverse. The opposing solution y is calculated for a given solution y according to the formula described in (6).…”

Section: Opposition Based Learningmentioning

confidence: 99%

“…The ABC algorithm has received a great interest in research community since it was discovered on account of its large applicability and simplicity. It was employed to solve different discrete and continuous optimization problems [6,7,18].…”

Section: Discrete Artificial Bee Colonymentioning

confidence: 99%

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A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

Ghoualmi

Benkechakche

Draa

2022

Preprint

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Recently, both industry and academics are paying increasingly more attention to bio-metric authentication using palm vein patterns due to the benefits it provides over traditional methods, like those that rely on a fingerprint, iris, or face. However, feature extraction of the palm vein feature, which seeks to collect the most distinctive features that uniquely identify individuals might have redundant and useless features, which would reduce the performance of the bio-metric system and cause a dimension issue. This paper presents an improved feature selection technique based on a bio-inspired artificial intelligence algorithm called Discrete Artificial Bee Colony (DABC) for palm vein bio-metric authen-tication. The best set of features from those offered by Gabor feature extractor are selected using the DABC algorithm. In attempt to grant additional diversity and effectively explore the solution space, a variant of Opposition Based Learning (OBL) notion is implemented. The proposed approach, termed Modified Opposition based DABC (MODABC), has been evaluated using the CASIA palm vein database in comparison to a number of feature selection algorithms based on bio-inspired artificial intelligence. The experimental findings demonstrate that the presented method perform better that the literature in a number of areas, including accuracy, FRR, FAR, feature vector size, and fitness values. The method being proposed enables providing a suitable balance between the feature vector size and the authentication rate.

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“…There are possibilities of misalignment between ROI pool and ROI which causes shift between original instances and generated mask. So, the combination of bitwise operators is used to reduce the gap between two different instances by using backbone structure of ResNet50 and ResNet101 (Nagarajan, Muthukumaran, Murugesan, Joseph, Meram, & Prathik, 2021; Zhang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Detecting breast cancer using novel mask R‐CNN techniques

Jahangeer

Thambidurai

2022

Expert Systems

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Breast cancer is more common in women and the mortality rate also increases in recent days. Early detection of breast cancer is reduces the severity of the disease to some extent. Various image processing and classification techniques imposed on a particular image to detect and diagnose the breast cancer clearly. For early detection, an efficient methodology is needed. To reduce death rate, accurate discovery of the disease should be efficiently implemented. The mask R-CNN is used for segmentation to identify abnormalities and ensemble CNN is used to classify the benign and malignant tumour from the given mammographic breast cancer image. The input is fed into the enhanced fuzzy based median filter which is used to remove speckle noises which in turn increases the clarity of images. The noise-free images are fed into the proposed mask R-CNN for segmentation. It is an effective framework for segmentation of medical images. The classification process is then given into ensemble based CNN classifier for prediction of mammogram images. The ensemble based CNN is proposed for the classification of mammograms as benign and malignant. The proposed method focus on the optimization which is performed to minimize the memory requirements and running time while maintaining the high performance of the classifier. The performance metrics such as accuracy, precision, f-measure, and recall is evaluated to measure the efficacy of the proposed design. Using MIAS and DDSM dataset the performance metrics has been evaluated and compared with existing approaches. The results shows that the performance of the proposed method shows higher efficiency.

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Innovative feature selection and classification model for heart disease prediction

Cited by 48 publications

References 24 publications

Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy

Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

Detecting breast cancer using novel mask R‐CNN techniques

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