Ensemble Methods for Heart Disease Prediction

Karadeniz, Talha; Tokdemir, Gül; Maraş, Hadi Hakan

doi:10.1007/s00354-021-00124-4

Cited by 23 publications

(13 citation statements)

References 23 publications

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“…Another study [ 34 ] used ModifiedBoostARoota-CatBoost (MBAR-CB), CatBoost classifiers (CBC), ModifiedBoostARoota-XGBoost (MBAR-XGB), XGBoost Classifier (XGBC) for CHD, SHD, and SAHDD, respectively. The study [ 35 ] proposed an ensemble learning approach for heart disease prediction. The authors conducted experiments on the SHD and achieved an 0.88 accuracy score using the proposed ensemble model.…”

Section: Resultsmentioning

confidence: 99%

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

et al. 2022

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Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets.

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Section: Resultsmentioning

confidence: 99%

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

et al. 2022

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“…Variety of applications of the XGBoost have been cited in many application domains and the approach works well for smallto-medium sized structured data [30]. Boosting is a machine learning method that sequentially adds several different weak learners to realize a strong learner with higher computing power [31]. Weak learners are added repetitively by using a gradient descent algorithm until a strong learner with noticeable performance is achieved.…”

Section: Non-infectious Classmentioning

confidence: 99%

Early Urine Infection Prediction Framework using XGBoost Ensemble Model in IoT-Fog Environment

Gupta

Singh

2022

Preprint

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Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoTbased sensors for data collection, followed by data encoding and Infectious Risk Factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the healthrelated information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out and results are calculated based on real-time patients’ data. The statistical results of accuracy(91.45%), specificity(95.96%), sensitivity(84.79%), precision(95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.

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“…Feature selection is used to select the feature by a condition from the original feature that is relevant to the application. The algorithms intended for collecting features according to dissimilar evaluation measures fall into three classes: filters, wrappers, and hybrid models [9][10][11].…”

Section: Fs-feature Selectionmentioning

confidence: 99%

Cardio Vascular Diseases Detection Using Ultrasonic Image by Retaining Deep Learning Model

Damodharan

Goel

2022

IJEER

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Nowadays people are taking more care of their health and lifestyle. At the same time, diseases affected probability also increased even at most one of the deadly diseases is cardiovascular disease. Earlier prediction and diagnosis are the only solution for resolving the issues. To identify deep language models will be used to predict issues efficiently in the earliest stage in the affected location. In this paper, we recommend an Enhanced DCNN model to classify and segment the issue in affected areas using ultrasonic Images. The model has three layers for the primary layer will train the input and passed the hidden layer. The secondary layer will classify the image based on the model and dataset using the convolution layer and finally the affected area presented by the bound box. This model shows the more accurate result on both training and testing data. And this method shows better results with 94% of accuracy are provides while compared to the existing method.

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Ensemble Methods for Heart Disease Prediction

Cited by 23 publications

References 23 publications

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

Early Urine Infection Prediction Framework using XGBoost Ensemble Model in IoT-Fog Environment

Cardio Vascular Diseases Detection Using Ultrasonic Image by Retaining Deep Learning Model

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