Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review
17 Feb 2016-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 136, Iss: 2, pp 43-51
TL;DR: The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper and it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system.
Abstract: The Healthcare trade usually clinical diagnosis is ended typically by doctor’s knowledge and practice. Computer Aided Decision Support System plays a major task in medical field. Data mining provides the methodology and technology to alter these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. Among the increasing research on heart disease predicting system, it has happened to significant to categories the research outcomes and gives readers with an outline of the existing heart disease prediction techniques in each category. Data mining tools can answer trade questions that conventionally in use much time overriding to decide. In this paper we study different papers in which one or more algorithms of data mining used for the prediction of heart disease. As of the study it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system. The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper.
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TL;DR: This paper proposes a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease with the hybrid random forest with a linear model (HRFLM).
Abstract: Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).
783 citations
Cites result from "Heart Disease Prediction System usi..."
...We will see later on how our results prove to be prominent when compared to some of the known supervised learning techniques [5], [17]....
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01 Nov 2020
TL;DR: This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm, using the existing dataset from the Cleveland database of UCI repository of heart disease patients.
Abstract: Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.
144 citations
Cites background from "Heart Disease Prediction System usi..."
...Minkowski distance, Manhattan distance or Hamming distance, Euclidean’s distance [17]....
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Book•
01 Jan 2001
TL;DR: Inelastic scattering in Electron Microscopy-Effects, Spectrometry and Imaging as discussed by the authors, Quantitative Analysis of High-Resolution Atomic Images, Electron Crystallography-Structure determination by combining HREM, Crystallographic image processing and electron diffraction.
Abstract: 1 The Modern Microscope Today.- 2 The Quest for Ultra-High Resolution.- 3 Z-Contrast Imaging in the Scanning Transmission Electron Microscope.- 4 Inelastic Scattering in Electron Microscopy-Effects, Spectrometry and Imaging.- 5 Quantitative Analysis of High-Resolution Atomic Images.- 6 Electron Crystallography-Structure determination by combining HREM, Crystallographic image processing and electron diffraction.- 7 Electron Amorphography.- 8 Weak-Beam Electron Microscopy.- 9 Point Group and Space Group Identification by Convergent Beam Electron Diffraction.- 10 Advanced Techniques in TEM Specimen Preparation.
107 citations
TL;DR: In this article, the spatial relationship between gully erosion and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory was assessed.
Abstract: Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.
104 citations
TL;DR: The benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones is concluded.
Abstract: Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 997073, 980488 and 972683% respectively A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 938537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones
83 citations
Cites background from "Heart Disease Prediction System usi..."
...Authors in [10] introduced particle swarm optimization to generate evolutionary values for HD, also good classification accuracy for HD dataset was presented in [11], in the form of a comparative analysis of different machine learning algorithms for diagnosis of heart disease as a survey paper, and it showed the suitability of machine learning algorithms and tools to be used for the analysis of HD, and decision-making process accordingly....
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References
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Book•
01 Jan 2008TL;DR: This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding.
Abstract: This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining.Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.
1,065 citations
31 Mar 2008
TL;DR: This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network, which shows that each technique has its unique strength in realizing the objectives of the defined mining goals.
Abstract: The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
572 citations
TL;DR: A new method for classification of data of a medical database is presented and one of the best results compared with results obtained from related previous studies and reported in the UCI web sites is observed.
Abstract: Data can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites.
365 citations
"Heart Disease Prediction System usi..." refers methods in this paper
...[39] Classification, Backpropagation, Fuzzy neural network techniques used for diseases diabetes and heart diseases....
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TL;DR: In this article, the use of fuzzy sets in map accuracy assessment expands the amount of information that can be provided regarding the nature, frequency, magnitude, and source of errors in a thematic map.
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327 citations
"Heart Disease Prediction System usi..." refers background in this paper
...Fuzzy membership assumes that membership to a given category will range from entire membership (100%) to nonmembership (0%), and that dataset may be classified as partial members into two or more categories [1]....
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TL;DR: This paper has analysed prediction systems for Heart disease using more number of input attributes and shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
Abstract: Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
292 citations