scholarly journals Tuned Random Forest Algorithm for Improved Prediction of Cardiovascular Disease

2020 ◽  
Vol 9 (1) ◽  
pp. 1355-1360
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Random Forest ◽  
Prediction Algorithm ◽  
Classification Algorithms ◽  
Prediction System ◽  
Random Forest Algorithm ◽  
High Error Rate ◽  
Accuracy Of Prediction ◽  
Selection Algorithms

Data mining is becoming more and more popular and essential in the field of medicine. The large amounts of data produced everyday by the medical industry are very complex and voluminous to be processed and analyzed by the usual traditional means. In such cases data mining comes into play. Despite the presence of several prediction algorithms, the efficiency is questionable due to the presence high error rate. Therefore it is necessary to choose a prediction algorithm that gives higher accuracy with fewer errors. The aim of this paper is to create a system for efficient and accurate prediction of cardiovascular disease. The datasets for the process is taken from UCI machine learning repository. The datasets are tested for accuracy using ANOVA technique. The algorithms are investigated using the WEKA tool. The best features for prediction are obtained from feature selection algorithms. Various classification algorithms are applied on the datasets to identify the most efficient algorithm. We observe that random forest gives consistently better accuracy than other algorithms. Tuning is done on the random forest algorithm to further improve the accuracy of prediction system.

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 Cardiovascular Disease Prediction System Using Extra Trees Classifier

2019 ◽  
Author(s):  
Rahman Shafique ◽  
Arif Mehmood ◽  
Saleem ullah ◽  
Gyu Sang Choi
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Support Vector ◽  
Prediction System ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Tree Classifier

Abstract Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

scholarly journals A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia

2021 ◽  
Vol 8 ◽  
Author(s):  
Guan Wang ◽  
Yanbo Zhang ◽  
Sijin Li ◽  
Jun Zhang ◽  
Dongkui Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Random Forest ◽  
Prediction Model ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Random Forest Algorithm

Objective: Preeclampsia affects 2–8% of women and doubles the risk of cardiovascular disease in women after preeclampsia. This study aimed to develop a model based on machine learning to predict postpartum cardiovascular risk in preeclamptic women.Methods: Collecting demographic characteristics and clinical serum markers associated with preeclampsia during pregnancy of 907 preeclamptic women retrospectively, we predicted the cardiovascular risk (ischemic heart disease, ischemic cerebrovascular disease, peripheral vascular disease, chronic kidney disease, metabolic system disease or arterial hypertension). The study samples were divided into training sets and test sets randomly in the ratio of 8:2. The prediction model was developed by 5 different machine learning algorithms, including Random Forest. 10-fold cross-validation was performed on the training set, and the performance of the model was evaluated on the test set.Results: Cardiovascular disease risk occurred in 186 (20.5%) of these women. By weighing area under the curve (AUC), the Random Forest algorithm presented the best performance (AUC = 0.711[95%CI: 0.697–0.726]) and was adopted in the feature selection and the establishment of the prediction model. The most important variables in Random Forest algorithm included the systolic blood pressure, Urea nitrogen, neutrophil count, glucose, and D-Dimer. Random Forest algorithm was well calibrated (Brier score = 0.133) in the test group, and obtained the highest net benefit in the decision curve analysis.Conclusion: Based on the general situation of patients and clinical variables, a new machine learning algorithm was developed and verified for the individualized prediction of cardiovascular risk in post-preeclamptic women.

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 Comparison of Some Classification Algorithms for the Analysis of Students Academic Performance in Educational Data Mining Using Orange

2021 ◽  
pp. 318-324
Author(s):  
Vanthana V
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Random Forest ◽  
Educational Data Mining ◽  
Evaluation Process ◽  
Support Vector ◽  
Classification Algorithms ◽  
Academic Improvement ◽  
Academic Information ◽  
Tools And Techniques

In the modern education system, many higher education institutions prefer data mining tools and techniques to analyze the academic improvement of their students. To support that many data mining techniques and tools are available. This paper uses the classification concept to analyze the student’s academic performance. This paper presents the comparison result of five classification algorithms – Decision Tree, Naïve Bayesian, K-Nearest Neighbour, Support Vector Machine and Random Forest which is applied to the data collected from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. The data mining tool used was ORANGE. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in the dataset. The results showed that Random Forest out performs the other classifiers based on accuracy.

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 Prognosis of Cancer and Proposition of Therapeutics

2020 ◽  
Vol 9 (8) ◽  
pp. 394-397
Keyword(s):  
Random Forest ◽  
Genetic Factors ◽  
Attribute Selection ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Random Forest Algorithm ◽  
Cancer Prediction ◽  
Vector Machines ◽  
The Common ◽  
Selection Algorithms

Cancer is becoming one of the common diseases in day today life, identifying it in a prior stage is still difficult. Identification of environmental and genetic factors is necessary to predict the cancer. We developed a cancer prediction system to predict lung and oral cancer based on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this system is to predict the type of cancer and the suggested therapy using random forest algorithm.

scholarly journals Educational Data Classification and prediction using Data Mining Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 8674-8678 ◽  
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Predictive Modeling ◽  
Research Work ◽  
Logical Structure ◽  
Individual Performance ◽  
Mining Machine ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Set

Data Mining is the process of extraction interesting patterns from huge data sets and converts the patterns into logical structure for further Analysis. Predictive Modeling processes that make use of data mining, Machine learning and probability methods to forecast. Engineering is the most widely accepted stream of education in India. Students are uncertain about which department to join in engineering. It is important to improve the individual performance and help the students make the perfect choice regarding the department. In this paper, the hidden information from the previously recorded enrollment details during admission process is used to solve the students’ uncertainty in their choice of department. In addition to this, the performance of alumnae also needs to be analyzed by the teachers to have a clear idea about the future of existing students. Our main goal is to unravel these problems using predictive Modeling. Here, we are focusing on three classification algorithms namely, support vector machine, Random Forest and Naïve Bayes. Data has been collected, normalized and applied to the three different classification algorithms, from which the best model is formulated using various parameters of evaluation. In this paper, we present our approach towards implementing the best model which is built based on the profession of parents, demographic features, type of location of the student and correlation between high school and higher secondary examinations. The Result of this research work shows that Random forest is efficient for the data set used when compared to the other two Classification algorithms.

scholarly journals Comparison with Classification Algorithms in Data Mining of a Fuel Automation System's Sales Data

2020 ◽  
Vol V (I) ◽  
pp. 245-254
Author(s):  
Ilhan Tarimer ◽  
Buse Cennet Karadag
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Random Forest ◽  
Naive Bayes ◽  
Data Classification ◽  
Classification Algorithms ◽  
Sales Data ◽  
Station Data ◽  
Classification Procedures ◽  
Accuracy Rates

This article deals with Otobil and pumps sales estimates at fuel stations. The fuel station data used in the study consists of 2384 data in total. Depending upon these data, classification procedures were performed on fuel station sales data using classification algorithms. In the study the classification algorithms that J48, Random Forest, KStar, Logistic Regression, IBk and Naive Bayes algorithms are used to compare the sales data estimations by using a software. The results obtained show that the accuracy rates of the J48 algorithm are more successful than others in general. It understands that these sales estimations shall encourage fuel station owners and association bodies to get more gainful.

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