Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach

Krishnaiah, V.V. Jaya Rama; Narsimha, G.; Chandra, Naveen

doi:10.1007/978-3-319-13728-5_42

Cited by 50 publications

(22 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long et al [14] proposed a diagnostic system for heart disease which is based on attribute reduction of rough sets using chaos firefly algorithm and interval type-2 fuzzy logic by re-defining the dimensions and performed validation over the heart disease dataset. Krishnaiah et al [15] have used the minimum distance-based fuzzy-kNN classifier to diagnose heart disease and the reported classification accuracy is 91%. Iftikhar et al [17] used SVM and particle swarm optimization (PSO) to create an analytical model for health care.…”

Section: State-of-the-artmentioning

confidence: 99%

MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis

et al. 2020

View full text Add to dashboard Cite

Cardiovascular disease tops the list among all major causes of deaths worldwide. Though, prognostication and in-time diagnosis can help in reducing the mortality rate as well as increases the survival rate of patients. Unavailability or scarcity of radiologists and doctors in different countries due to several reasons is a significant factor for hindrance in early diagnosis. Among various efforts of developing the decision support systems, computational intelligence is an emerging trend in the field of medical imaging to detect, prognosticate and diagnose the disease. It helps radiologists and doctors to get relief from being over-burdened and minimizes the induced delays for in-time diagnosis of patients. In this work, a machine intelligence framework for heart disease diagnosis MIFH has been proposed. MIFH utilizes the factor analysis of mixed data (FAMD) to extract as well as derive features from the UCI heart disease Cleveland dataset and train the machine learning predictive models. The framework MIFH is validated using the holdout validation scheme. Experimentation results show that MIFH performed well over several baseline methods of recent times in terms of accuracy and comparable in terms of sensitivity and specificity. MIFH returns best possible solution among all input predictive models considering performance criteria and improves the efficacy of the system, hence can assist doctors and radiologists in a better way to diagnose heart patients.

show abstract

Section: State-of-the-artmentioning

confidence: 99%

MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Authors compared this system with regression model that obtain 92.0% accuracy.V. Krishnaiah et al [15] present a model for heart disease prediction by removing the redundancy of the data using Fuzzy K-NN classifier with the accuracy of 91%. A combination of genetic algorithm and recurrent fuzzy neural networks based classifier is applied on Cleveland data and achieved 97.78% accuracy [14].…”

Section: Related Workmentioning

confidence: 99%

Morality Prediction Model in Cardiovascular Disease with Significant Feature Selection and Hybrid KNN Classification Technique

Sowmiya*¹,

Sumitra²

2019

IJITEE

View full text Add to dashboard Cite

Nowadays morality rate is increasing globally due to heart disease. It is one of the leading health risk facing men today. So early detection of heart disease assist the patients to maintain a healthy life style. Several techniques are used in the medical field to detect or diagnose disease in view of patient family health history and some other aspects. However, developing a system to predict the heart diseases without any medical tests is still challenging. Machine learning (ML) approaches is suitable and effective in providing decision and prediction from enormous health care data. Several previous researches provide an overall view in ML methods for disease prediction but the accuracy of prediction is still needed to be improved. In this study, a novel framework is presented that intent at removing the unwanted features with Bacterial Colony Optimization algorithm and applies the Hybrid KNN algorithm with great accuracy in identifying the heart disease. This prediction model is developed with UCI Cleveland dataset with several known classification approaches. An enhanced model is presented with 99.83% accuracy in heart disease prediction. The presented study is compared with other classification approaches.

show abstract

“…V. Krishaiah et al have developed a fuzzy KNN based prophetic system for heart diseases. Authors used fuzzy approach to remove the uncertainty and to improve the accuracy level of prediction related to heart patients [28]. Bashir et al have proposed an innovative ensemble classifier based upon five different techniques viz.…”

Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Sharma

Singh

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

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.

show abstract

Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach

Cited by 50 publications

References 8 publications

MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis

MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis

Morality Prediction Model in Cardiovascular Disease with Significant Feature Selection and Hybrid KNN Classification Technique

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Contact Info

Product

Resources

About