A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh, T.; Assegie, Tsehay Admassu; Rajkumar, Subhashni; Kumar, N. Komal

doi:10.11591/ijece.v12i2.pp1831-1838

Cited by 22 publications

(15 citation statements)

References 25 publications

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“…Figure 1 demonstrates the correlation circle for heart disease features. Pearson correlation among each heart disease feature is determined by using the Pearson's correlation formula given in (1) [22]- [24]:…”

Section: Correlation Modelmentioning

confidence: 99%

An empirical study on machine learning algorithms for heart disease prediction

Assegie¹,

Kumar

et al. 2022

IJ-AI

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In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.

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Section: Correlation Modelmentioning

confidence: 99%

An empirical study on machine learning algorithms for heart disease prediction

Assegie¹,

Kumar

et al. 2022

IJ-AI

Self Cite

View full text Add to dashboard Cite

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“…Hyperplane has been used to classify the most significant of the closest data points into two separate classes. HD prediction using a PSO-based SVM algorithm outperforms the DT, NB, NN, and SVM by a factor of 100 [19]. The PRC and cm plot for the SVM model that predicts thirty-five HD patients has been shown in Figure 8 The neural network (NN) algorithm is based on biological neural networks and aims to mimic the nervous system of humans in the learning process.…”

Section: F Support Vector Machinementioning

confidence: 99%

Comparative analysis and prediction of coronary heart disease

Prusty

Patnaik

Dash

2022

IJEECS

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Cardiovascular disease (CVD) <span>is now one of the leading causes of death worldwide and was also thought to be a serious illness in the mid and old ages. Artificial intelligence and machine learning have a huge impact on the healthcare areas. As a result, getting a familiar individual with data processing techniques suitable for numerical health data. Although, the most often used algorithms for classification tasks will be incredibly advantageous in terms of time management. In particular here, a common procedure has been proposed for predicting cardiovascular disease. Accordingly, we herein consider nine typical classifiers of both machine learning and deep learning technology for the comparative analysis and prediction of coronary heart failure. These models are computationally inexpensive and easy to build. Moreover, these classifiers are tested and compared using a confusion matrix in the Jupyter notebook, yielding classification measures such as accuracy, f1-score, recall, and precision. As a result, the logistic regression classifier gives the maximum possible accuracy, precision, and f1-score of 90.78%, 90.24%, and 91.35% respectively.</span>

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“…These systems which began to commercialize a few years ago experienced dramatic progress in the development of applications for businesses and industries [1]- [7]. Furthermore, mobile applications were implemented in the agricultural and medical fields for diagnosis and treatment [8]- [11]. Therefore, it was built to act as a decision support system [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

Development of a mobile expert system for the diagnosis on motorcycle damage using forward chaining algorithm

Naryanto

Delimayanti

Kriswanto

et al. 2022

IJEECS

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Indonesia is an <span>ASEAN country with the most motorcycle users, where one-third of its population own motorcycles. The automatic motorcycle is the most widely used type due to its agility and fuel-efficient abilities. Sudden motorcycle damage could hamper the users' activities. However, most of these users do not know the reason for the damage. This paper presents the development of expert system for diagnosing the damage of motorcycle using forward chaining method. This system was implemented in mobile application. Through a mobile application, a solution for these users can be obtained. This application immediately discovers the damaged location and repair process. Furthermore, it acts as the first solution before motorcycle repair is carried out in a shop. In this study, the forward chaining method was implemented. It is based on a pattern-matching algorithm whose primary objective is to match facts (input data) with appropriate rules from the rule base. Various test results showed that the diagnostic application used for automatic motorcycle damages 100% worked.</span>

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A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Cited by 22 publications

References 25 publications

An empirical study on machine learning algorithms for heart disease prediction

An empirical study on machine learning algorithms for heart disease prediction

Comparative analysis and prediction of coronary heart disease

Development of a mobile expert system for the diagnosis on motorcycle damage using forward chaining algorithm

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