A Hybrid Approach for Heart Disease Prediction

2021 ◽  
pp. 95-101
Author(s):  
Aamir Khan ◽  
Dr. Sanjay Jain
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Heart Disease ◽  
Random Forest ◽  
Research Work ◽  
Hybrid Approach ◽  
Disease Prediction ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Analysis Of Performance

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

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 Testing the Static-99R as a Global Screen for Risk of Sex Crime Recidivism in a Norwegian Routine Sample

Sexual Abuse ◽  
2020 ◽  
pp. 107906322095119
Author(s):  
Ingeborg Jenssen Sandbukt ◽  
Torbjørn Skardhamar ◽  
Ragnar Kristoffersen ◽  
Christine Friestad
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Random Forest ◽  
Predictive Accuracy ◽  
Proportional Hazard ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Sex Offense ◽  
Observation Period ◽  
Sex Crime

The Static-99R has been recommended for use as a first global screen for sorting out sex-convicted persons who are in need of further risk assessment. This study investigated the Static-99R’s predictive validity based on a nonselected Norwegian sample ( n = 858) of persons released from prison after having served a sex crime sentence. After a mean observation period of 2,183 days, 3.4% ( n = 29) had recidivated to a new sex offense. A higher number of recidivists were found among those with higher Static-99R total scores. The predictive contribution from each of the ten Static-99R risk items was investigated using standard logistic regression, proportional hazard regression, and random forest classification algorithm. The overall results indicate that the Static-99R is relevant as a risk screen in a Norwegian context, providing similar results concerning predictive accuracy as previous studies.

Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

2021 ◽  
pp. 234-259
Author(s):  
Nitika Kapoor ◽  
Parminder Singh
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Forest Classification ◽  
Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

scholarly journals Loan Amount Prediction Using Machine Learning

2021 ◽  
pp. 212-215
Author(s):  
Bhagyashri Rajesh Jawale ◽  
Priyanka Anil Badgujar ◽  
Rita Dnyaneshwar Talele ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Research Work ◽  
Ensemble Methods ◽  
Classification Algorithm ◽  
Optimization Techniques ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Or Organization ◽  
Loan Amount

Loan amount prediction is helpful for banks or organization who want their work easier. All Banks give Loan to customer and customer first apply for loan after any bank or organization validate customer information. It must be providing some advantages for banks or company or any organization who wants to give loan. There are various methods to improve the accuracy classification algorithm. The accuracy of random forest classification algorithm can be improved using Ensemble methods. Optimization techniques and Feature selection methods available. In this research work novel hybrid feature selection algorithm using wrapper model and fisher introduced. The main objective of this paper is to prove that new hybrid model produces better accuracy than the traditional random forest algorithm.

scholarly journals A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

2022 ◽  
Vol 12 (2) ◽  
pp. 1831
Author(s):  
Tamilarasi Suresh ◽  
Tsehay Admassu Assegie ◽  
Subhashni Rajkumar ◽  
Napa Komal Kumar
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Random Forest ◽  
Hybrid Model ◽  
Regularization Parameter ◽  
Hybrid Approach ◽  
Experimental Result ◽  
Medical Decision ◽  
Support Vector ◽  
Disease Prediction

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

Sign in / Sign up

Export Citation Format

Share Document