Early Heart Disease Prediction Using Data Mining Techniques

Methaila, Aditya; Kansal, Prince; Arya, Himanshu; Kumar, Pankaj

doi:10.5121/csit.2014.4807

Cited by 57 publications

(25 citation statements)

References 6 publications

(1 reference statement)

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“…We have evaluated the prediction of machine learning algorithms, and obtained a very high accuracy rate. Machine learning has been used for prediction and diagnosis of several diseases, e.g., Parkinson's [9], cancer [10] and heart disease [11]. Among machine learning methods, Support Vector Machines (SVM) [12] have been used in malaria incidence prediction [13]; but this study has several shortcomings: (i) the dataset used was extremely small (the size is only 33), which makes accuracy of prediction questionable; (ii) the dataset was used without analysing ecological factors, which could result in the inclusion of statistically insignificant variables in the prediction model, and hence could cause overfitting; (iii) there is no systematic methodology to transform this predictor into a smart healthcare system.…”

Section: Introductionmentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

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Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.

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Section: Introductionmentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

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“…Methaila et al [17] In their examination work focused on using different counts and mixes of a couple of target qualities for amazing heart ambush Figure using data mining. Decision Tree has beated with 99.62% precision by using 15 characteristics.…”

Section: Theoretical and Empirical Reviewmentioning

confidence: 99%

A Model for Coronary Heart Disease Prediction Using Data Mining Classification Techniques

Makumba

Cheruiyot

Ogada

2019

AJRCoS

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Nowadays the guts malady is one amongst the foremost causes of death within the world. Thus it s early prediction and diagnosing is vital in medical field, which might facilitate in on time treatment, decreasing health prices and decreasing death caused by it. The treatment values the disease is not cheap by most of the patients and Clinical choices are usually raised supported by doctors" intuition and skill instead of on the knowledge-rich information hidden within the stored data. The model for prediction of heart disease using a classification techniques in data mining reduce medical errors, decreases unwanted exercise variation, enhance patient well-being and improves patient results. The model has been developed to support decision making in heart disease prediction based on data mining techniques. The experiments were performed using the model, based on the three techniques, and their accuracy in prediction noted. The decision tree, naïve Bayes, KNN (K-Nearest Neighbors) and WEKA API (Waikato Environment for Knowledge Analysisapplication programming interface) were the various data mining methods that were used. The model predicts the likelihood of getting a heart disease using more input medical attributes. 13 attributes that is: blood pressure, sex, age, cholesterol, blood sugar among other factors such as Makumba et al.; AJRCOS, 3(4): 1-19, 2019; Article no.AJRCOS.50129 2 genetic factors, sedentary behavior, socio-economic status and race has been use to predict the likelihood of patient getting a Heart disease until now. This study research added two more attributes that is: Obesity and Smoking.740 Record sets with medical attributes was obtained from a publicly available database for heart disease from machine learning repository with the help of the datasets, and the patterns significant to the heart attack prediction was extracted and divided into two data sets, one was used for training which consisted of 296 records & another for testing consisted of 444 records, and the fraction of accuracy of every data mining classification that was applied was used as standard for performance measure. The performance was compared by calculating the confusion matrix that assists to find the precision recall and accuracy. High performance and accuracy was provided by the complete system model. Comparison between the proposed techniques and the existing one in the prediction capability was presented. The model system assists clinicians in survival rate prediction of an individual patient and future medication is planned for. Consequently, the families, relatives, and their patients can plan for treatment preferences and plan for their budget consequently. Minireview Article

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“…A proposed system done by Abhishek Taneja uses data mining technologies to diagnose heart disease and developing a cost-effective treatment system [1]. Where Aditya Methaila, Kansal, Arya and Kumar intend to use data mining tools for Classification Modeling Techniques to predict and diagnose heart disease [2]. A system constructed by Safwan O. and Yaser A. Jasim uses artificial neural networks and data mining tool to diagnose and repair application errors [7].…”

Section: Related Workmentioning

confidence: 99%

Developing a Software for Diagnosing Heart Disease via Data Mining Techniques

Jasim

Saeed

2018

ADCAIJ

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Data mining; Artificial Neural Network; Matlab R2016a and Heart diseaseThis paper builds a data mining tool via a classification method using Multi-Layer Perceptron (MLP) with Backpropagation learning method and an algorithm of feature selection along with biomedical testing values for diagnosing heart disease. Addition to that, developing a prototype for heart disease diagnosing with a friendly-user graphical interface (GUI). The purpose to construct this software is that; clinical prosopopoeia is done in any event by doctor's experience. Despite that, some cases are reported negative diagnosis and treatment; therefore, patients are asked to take a number of tests for diagnosis. Moreover, not all the tests contribute towards an effective diagnosis of a disease, and by using data mining approach to diagnose heart disease that supports the doctors to make more efficient and subtle decisions.

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Early Heart Disease Prediction Using Data Mining Techniques

Abstract: ABSTRACT

Cited by 57 publications

References 6 publications

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

A Model for Coronary Heart Disease Prediction Using Data Mining Classification Techniques

Developing a Software for Diagnosing Heart Disease via Data Mining Techniques

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