Heart Disease Prediction Using Machine Learning Techniques

Barik, Shekharesh; Mohanty, Sambit K.; Rout, Deepankar; Mohanty, Subhra; Patra, Akshaya Kumar; Mishra, Alok Kumar

doi:10.1007/978-981-15-5262-5_67

Cited by 24 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e rationale to apply a decision tree is to develop a heart disease risk evaluation model that can predict a class (diseased or healthy) by learning simple decision rules deduced from training data [25].…”

Section: Experimental Results Of Decision Tree Modelmentioning

confidence: 99%

See 1 more Smart Citation

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques

Ansarullah

Saif

Kumar

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Introduction. Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions. Purpose. Stimulated by the increasing heart disease mortality rate incidents, an effective, low-cost, and reliable heart disease risk evaluation model is developed using significant non-invasive risk attributes. The significant non-invasive risk attributes like (age, systolic BP, diastolic BP, BMI, hereditary factor, smoking, alcohol, and physical inactivity) are identified by the help of medical domain experts, and their reliability in heart disease prediction is investigated through different feature selection techniques. Methodology. The enhancements of applying specific investigated techniques like random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor to the risk factors are tested. The heart disease risk assessment model is developed using the Jupyter Notebook web application, and its performance is tested not only through medical domain measures but also through the model performance measures. Findings. To evaluate heart disease risk evaluation model, we calculated measures of discrimination like error rate, AUROC, sensitivity, specificity, accuracy, precision, and so on. Experimental results show that the random forest heart disease risk evaluation model outperforms other existing risk models with admirable predictive accuracy and minimum misclassification rate. Originality. The heart disease risk evaluation model is developed based on novel non-invasive heart disease dataset, which consists of 5776 records. This dataset is collected from different heterogeneous data sources of Kashmir (India) through quantitative data collection methods. Research Implications. The risk model is applicable where people lack the facilities of integrated primary medical care technologies for untimely heart disease risk prediction. Future Work. To investigate deep learning and study the significance of other controlled attributes on different age and sex groups in the risk estimation of heart disease.

show abstract

Section: Experimental Results Of Decision Tree Modelmentioning

confidence: 99%

“…e performance of the model is evaluated on Cleveland heart disease dataset, and the results are compared with different existing models. Barik et al [25] applied decision tree, optimized decision tree, random forest, and other algorithms to predict heart disease at its initial stages.…”

Section: Literature Reviewmentioning

confidence: 99%

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques

Ansarullah

Saif

Kumar

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Evaluation metrics are based on accuracy measure, precision, and recall, and concluded SVM better than other techniques with 89.34%. To assess the performance of the algorithm in terms of higher accuracy proposed by Barik et al [30] through comparative analysis on NB, DT, LR, and RF. The dataset obtained from the UCI dataset has 14 attributes and 303 instances.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Risk Prediction Expending of Classification Algorithms

Mary¹,

Khan²,

Asiri³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today's lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease the error rate and enhance the accuracy of HDP assessment measures. However, in order to discover the optimal HDP solution, this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California, Irvine (UCI) machine learning repository. In a comparative analysis, Mean Absolute Error (MAE), Relative Absolute Error (RAE), precision, recall, fmeasure, and accuracy are used to evaluate Linear Regression (LR), Decision Tree (J48), Naive Bayes (NB), Artificial Neural Network (ANN), Simple Cart (SC), Bagging, Decision Stump (DS), AdaBoost, Rep Tree (REPT), and Support Vector Machine (SVM). Overall, the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate, with RAE of 33.2631 and MAE of 0.165, the precision of 0.841, recall of 0.835, f-measure of 0.833, and accuracy of 83.49 percent for the dataset gathered from UCI. The SC improves accuracy and reduces the error rate for the Kaggle dataset, which is 3.30% for RAE, 0.016 percent for MAE, 0.984% for precision, 0.984 percent for recall, 0.984 percent for f-measure, and 98.44% for accuracy.

show abstract

“…There are many studies conducted to predict future diseases using regression and artificial intelligence networks. ML techniques have been used to predict chronic diseases [9], diabetes [10], heart disease [11], Breast Cancer [12] and other diseases to help the doctor make the right decision.…”

Section: Related Workmentioning

confidence: 99%

Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

Hassan¹,

Qasem²,

Abdalla³

et al. 2021

IJITCS

View full text Add to dashboard Cite

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

show abstract

Heart Disease Prediction Using Machine Learning Techniques

Cited by 24 publications

References 6 publications

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques

Heart Disease Risk Prediction Expending of Classification Algorithms

Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

Contact Info

Product

Resources

About