scholarly journals Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 963-967
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Early Diagnosis ◽  
Decision Tree ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Random Forest Algorithm ◽  
Medical Field ◽  
Data Mining Techniques

Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.

scholarly journals Efficient Data Mining Techniques for Heart Disease Prediction and Comparative Analysis of Classification Algorithms

2021 ◽  
pp. 57-68
Author(s):  
Md. Ashikur Rahman Khan ◽  
Masudur Rahman ◽  
Jayed Us Salehin ◽  
Md. Saiful Islam ◽  
Md. Fazle Rabbi
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Middle Income ◽  
Data Mining Techniques ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Efficient Data

Data mining techniques are used to extract interesting patterns and discover meaningful knowledge from huge amount of data. There has been increasing in usage of data mining techniques on medical data for determining useful trends and patterns that are used in analysis and decision making. About eighty percent of human deaths occurred in low and middle-income countries due to heart diseases. The healthcare industry generates large amount of heart disease data which are not organized. These data make the prediction process more complicated and voluminous. Data mining provides the techniques for fast and accurate transformation of data into useful information for heart diseases prediction. The main objectives of this research is to predict heart diseases more accurately using Naïve Bayes, J48 Decision Tree, Neural Network, Random Forest classification algorithms and compare the performance of classifiers. The research uses raw dataset for performance analysis and the analysis is based on Weka Tool. This research also shows best technique from them which is Random Forest on the basis of accuracy and execution time.

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

scholarly journals Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

2019 ◽  
Vol 9 (4) ◽  
pp. 2675
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span lang="EN-US">Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

Prediction of Heart Diseases Using Data Mining Techniques

2018 ◽  
Vol 3 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Nancy Masih ◽  
Sachin Ahuja
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Complete Analysis ◽  
Support Vector ◽  
Classification Algorithms ◽  
Process Data ◽  
Data Mining Techniques ◽  
Healthcare Data ◽  
Hidden Patterns

Health care organizations accumulate large amount of healthcare data, but it is not ‘extracted' to draw hidden patterns which can prove efficient for the decision making process. Data mining techniques can be used to gain insights by discovering hidden patterns which remain undetected manually. Data analytics proves to be useful in detection and identification of the diseases. A complete analysis has been conducted on the FHS (Framingham Heart Study) using various data analytic techniques viz. Decision tree, Naïve Bayes, Support vector machine (SVM) and Artificial neural network (ANN) and the results were ranked according to the accuracy. ANN produce better results than other classification algorithms. The output helps to find out the prominent features that cause heart disease and also identifies the most common features that must be analyzed for prediction of deaths due to heart disease. Despite various studies carried out on heart diseases, the main focus of this study is prediction of heart disease on the dataset of FHS by using various classification algorithms to achieve high accuracy.

scholarly journals Human Heart Disease Prediction System Using Data Mining Techniques

2019 ◽  
Vol 8 (II) ◽  
Author(s):  
Sidra Javed ◽  
Hamza Javed ◽  
Ayesha Saddique ◽  
Beenish Rafiq
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Heart Diseases ◽  
Heavy Load ◽  
Data Mining Techniques ◽  
Id3 Algorithm ◽  
Wide Range ◽  
Using Data ◽  
Human Heart Disease

— Prediction of heart disease is a big concern now a days because everyone is busy and due to heavy load of work people do not give attention to their health. To diagnose a disease is a big challenge. The issue is to extract data that have some meaningful knowledge. For this purpose, data mining techniques are used to extract meaningful data. Decision Tree and ID3 are used to predict heart diseases. Many researchers and practitioners are familiar with prediction of heart diseases and wide range of techniques is available to predict disease. To address this problem, Decision Tree is used to predict the heart disease. In this study the collected data is pre-processed, Decision Tree algorithm and ID3 were then applied to predict the heart disease.   Index Terms— Decision Tree, ID3 Algorithm, Data Mining, Decision Support System (DSS), knowledge Discovery from Databases (KDD).

scholarly journals Prediction of Heart Stroke using A Novel Framework – PySpark

2021 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Chitluri Sai Harish B ◽  
G gnana krishna vamsi ◽  
G jaya phani akhil ◽  
J n v hari sravan ◽  
V mounika chowdary
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Random Forest Algorithm ◽  
The World

Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.

scholarly journals Prediction of Heart Stroke using A Novel Framework – PySpark

2021 ◽  
pp. 1-4
Author(s):  
Mr. Chitluri Sai Harish ◽  
◽  
Mr. G gnana krishna vamsi ◽  
Mr. G jaya phani akhil ◽  
Mr. J n v hari sravan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Random Forest Algorithm ◽  
The World

Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.

scholarly journals Crime analysis in India using data mining techniques

2018 ◽  
Vol 7 (2.6) ◽  
pp. 253 ◽  
Author(s):  
Deepika K K ◽  
Smitha Vinod
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Random Forest ◽  
Crime Analysis ◽  
Random Forest Algorithm ◽  
Indian States ◽  
Data Mining Techniques ◽  
Crime Data ◽  
Crime Detection ◽  
Using Data

An approach for crime detection in India using Data mining techniques is proposed in this paper. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Data mining techniques are often applied to Criminology as it provides good results. Criminology is a field which studies about various crime characteristics. Analyzing crime data means exploring crime data. Crime is identified using k-means clustering and the clusters are formed based on the similarity of the crime attributes. The Random Forest algorithm and Neural networks are applied on the data for classification. Visualization is achieved using the Google marker clustering and the crime spots are marked on the India map. The accuracy is verified using WEKA tool. This approach will benefit the Crime department of India in analyzing crime with better prediction. The paper focuses on the crime analysis of various Indian states and union territories during 2001 to 2012.  

scholarly journals Prediction of active debt in the State of Pernambuco, Brazil

2020 ◽  
Vol 5 (1) ◽  
pp. 88-95
Author(s):  
Álvaro Farias Pinheiro ◽  
João Alberto Da Silva Amaral ◽  
Geraldo Torres Galindo Neto ◽  
José Nilo Martins Sampaio ◽  
Wedson Lino Soares
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
The State ◽  
Support Vector ◽  
Data Mining Techniques ◽  
Collection Process ◽  
Mining Model

Application of data mining (DM) techniques to optimize the process of collection of Active Debt (AD) of the State of Pernambuco, Brazil. We apply the following data mining techniques: Decision Tree (DT), Logistic regression (LR), Nayve bayes (NB), Support vector machine (SVM), also applied to the Random Forest technique which is considered an essemble method. We observed that the RF technique obtained better results than all the techniques of classification, reaching higher values in all metrics analyzed. We note that the creation of a data mining model to choose which debts can succeed in the collection process can bring benefits to the pernambuco government. With the application of RF technique, we obtained indexes above 85% in the evaluation of the metrics.

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