Efficient Heart Disease Prediction System

Purushottam,; Saxena, Kanak; Sharma, Richa

doi:10.1016/j.procs.2016.05.288

Cited by 153 publications

(38 citation statements)

References 5 publications

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“…The performance of this efficient heart based prediction system was compared with algorithms like svm, neural networks, part, multi layer perceptron etc. It was concluded that the system gave an accuracy of 86.7% which was highest in comparison with other algorithms [3]. Theresa Princy, J. Thomas et al (2016) have used the KNN and ID3 algorithm for prediction.…”

Section: Literature Surveymentioning

confidence: 97%

Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

Naik¹,

Naik²

2018

IJCA

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Section: Literature Surveymentioning

confidence: 97%

Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

Naik¹,

Naik²

2018

IJCA

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“…During neural network training, cross-validation was used for evaluating the model during its training. Purushottam et al [9] have formed a decision tree for the classification of heart disease. In the beginning data pre-processing was performed to get the clean data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…After that, the physician uses his experience for the diagnosis of the disease which is not always 100% correct. [9]Normally the physicians refer to the previous diagnosis decisions that they took in the past for any patient with the same physical parameters. These parameters evaluation for the identification of the disease generally makes the diagnosis process very much complicated.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Coronary Artery Disease using 1-D Convolutional Neural Network

Debabrata¹,

Kumar²,

Debabala³

2019

IJRTE

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Heart disease is treated as one of the noxious diseases at the present time. Coronary artery disease is a kind of heart syndrome which is statistically growing day by day in the society. It is very tough for medical practitioners to predict Coronary artery disease as it is a complicated task that needs experience and acquaintance. For the detection of the disease doctors normally prescribe various invasive and non-invasive methods like angiography, ECG and echocardiogram. These methods are very expensive and sometimes not able to discover a number of undiagnosed symptoms. Due to these, it is not possible to detect the disease accurately at an early stage. The medical sector today contains a number of useful data that is helpful to detect a disease accurately. Using this data many researchers have proposed a number of intelligent systems for the detection of the disease. In this work, a competent system is implemented using deep learning for the better detection of the disease. The system is constructed using Convolutional Neural Network. The system has three phases. In the first phase data cleaning, data imputation and important feature selection are performed. In the second phase model training and hyperparameter tuning is performed. Finally, in the last phase, the model prediction is performed using the test data. The data set used for experimentation is Cleveland, Hungary, Switzerland and Long beach heart disease data present in the UCI repository. The proposed system gives a classification accuracy of 96.49% during testing, which is highest among all the discussed methods.

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“…Authors evaluated the consequences of proposed techniques with apriori algorithm, IMSIA algorithm, semi-apriori algorithm and claimed that their algorithm works well as compared to other existing techniques [20]. Purushottam et al (2016) have designed original, pruned, non-duplicates, classified and polish rules for diagnosis of cardio disorders. Authors compared the accuracy of their efficient diagnostic system with other mining techniques like SVM, C4.5, MLP, RBF and neural network and found that the predictive accuracy obtained using proposed system is better than other methods [21].…”

Section: It-based Healthcare Solution For Cardiovascular Disordersmentioning

confidence: 99%

“…Purushottam et al (2016) have designed original, pruned, non-duplicates, classified and polish rules for diagnosis of cardio disorders. Authors compared the accuracy of their efficient diagnostic system with other mining techniques like SVM, C4.5, MLP, RBF and neural network and found that the predictive accuracy obtained using proposed system is better than other methods [21]. B. Venkatalakshmi, M.V Shivsankar stated untimely diagnose of a heart problem might lead to illness or even death in some cases.…”

Section: It-based Healthcare Solution For Cardiovascular Disordersmentioning

confidence: 99%

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Sharma

Singh

2018

ICST Transactions on Scalable Information Systems

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The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.

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Efficient Heart Disease Prediction System

Cited by 153 publications

References 5 publications

Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

Diagnosis of Coronary Artery Disease using 1-D Convolutional Neural Network

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

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