scholarly journals Heart Disease Prediction Using Hybrid Random Forest Model Integrated with Linear Model

2021 ◽  
Author(s):  
Jaishri Pandhari Wankhede ◽  
Palaniappan S ◽  
Magesh Kumar S
Keyword(s):  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Early Stage ◽  
Estimation Procedure ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Disease Prediction ◽  
Forest Model ◽  
Simple Estimation

The objective of the paper is to throw light on few existing heart disease predicting approaches and proposes a Hybrid Random Forest Model Integrated with Linear Model (HRFMILM) for predicting and identifying the HDs at an early stage. Even though the linear model has simple estimation procedure, it is very sensitive to outliers and may lead to overfitting process. On the other hand, averaging in Random Forest Model (RFM) improves the overall accuracy and reduces the possibility of overfitting. The dataset is collected from standard UCI repository. Experimental results concluded that the integration of Linear Model with RFM makes the simple estimation procedure with improved overall accuracy than the respective models. Further, the proposed method compares the prediction performance of few existing approaches in terms of parameters, namely, precision, recall and F1-score.

scholarly journals Heart Disease Prediction Based on Optimized Random Forest Model Using Machine Learning

2021 ◽  
pp. 337-340
Author(s):  
Prof. R. A. Jamadar ◽  
Aarati Garje ◽  
Tejasvi Bhorde ◽  
Vaishnavi Jadhav
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Failure Detection ◽  
High Accuracy ◽  
Random Forest Model ◽  
Learning System ◽  
Disease Prediction ◽  
Forest Model ◽  
Coronary Failure

Heart disease is one amongst the key causes of death now-a-days. Prediction of the center sickness is troublesome, time overwhelming and expensive, therefore we tend to try to beat it. This analysis is to assist individuals, as we all know prediction of upset may be a vital challenge and it’s expensive that most of the individuals can’t afford and lacking behind due to these, therefore to assist them for obtaining done this tests in low value, we tend to try to develop cardiovascular disease prediction system victimization machine learning. As there square measure several systems designed for machine-controlled coronary failure testing however it's some drawbacks like over fitting that we tend to try to beat in our system and implementing system which is able to show smart performance and have high accuracy as compared to alternative systems. Experiment is performed victimization on-line clinical coronary failure dataset. The projected methodology is a smaller amount complicated with high accuracy of report. They contributes towards study square measure as follows: one. AN intelligent learning system RSA-RF is projected for the machine-controlled detection of coronary failure. The projected RSA-RF model was projected and developed for the primary time for the center failure detection. Previously, RSA algorithms have shown winning applications in looking best hyper parameters of a model. This paper presents its application in looking best set of options. 2. The developed learning system improves coronary failure prediction of typical random forest model by three.3% and shows higher performance than eleven recently projected strategies and alternative state of the art machine learning models for coronary failure detection. Moreover, the projected methodology shows lower time complexness because it reduces the amount of options[1].

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

scholarly journals Development and internal validation of risk prediction model of metabolic syndrome in oil workers

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Health Management ◽  
Disease Risk ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Brier Score ◽  
Internal Validation ◽  
Forest Model ◽  
Oil Workers

Abstract Background The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people’s health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome. Methods Design existing circumstances research. A total of 1468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models. Results The results showed that the accuracy of the three models was 82.49,95.98 and 92.03%, the sensitivity was 87.94,95.52 and 90.59%, the specificity was 74.54, 96.65 and 94.14%, the F1 score was 0.86,0.97 and 0.93, and the area under ROC curve was 0.88,0.96 and 0.92, respectively. The Brier score of the three models was 0.15, 0.08 and 0.12, Observed-expected ratio was 0.83, 0.97 and 1.13, and the Integrated Calibration Index was 0.075,0.073 and 0.074, respectively, and explained how the random forest model was used for individual disease risk score. Conclusions The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

2020 ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Risk Prediction ◽  
Roc Curve ◽  
Prediction Models ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Forest Model ◽  
The Metabolic Syndrome ◽  
Oil Workers

Abstract Background. The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health.In this paper, three kinds of risk prediction models applicable to the metabolic syndrome of oil workers were established, and the optimal models were found through comparison. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Methods. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results. The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, and the area under the ROC curve was 0.894, 0.987 and 0.935, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, and the area under the ROC curve was 0.797, 0.861 and 0.855, respectively.Conclusions. The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals Development and internal validation of risk prediction model of metabolic syndrome in oil workers

2020 ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Risk Prediction ◽  
Roc Curve ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Optimal Model ◽  
Forest Model ◽  
The Metabolic Syndrome ◽  
Oil Workers

Abstract Background.The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Objective.To develop and internally verify three risk prediction models for the metabolic syndrome of petroleum workers, compare the prediction performance of the three models, and find the optimal model.Methods. Design existing circumstances research. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results.The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, the area under the ROC curve was 0.894, 0.987 and 0.935, and the Integrated Calibration Index was 0.074, 0.071 and 0.078, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, the area under the ROC curve was 0.797, 0.861 and 0.855, and the Integrated Calibration Index was 0.064, 0.051 and 0.096, respectively.Conclusions.The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals A zero altered Poisson random forest model for genomic-enabled prediction

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Brandon A Mosqueda-Gonzalez ◽  
José Cricelio Montesinos-López ◽  
José Crossa ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Statistical Machine Learning ◽  
Excess Zeros ◽  
Forest Model ◽  
Generalized Poisson ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Count Response

Abstract In genomic selection choosing the statistical machine learning model is of paramount importance. In this paper, we present an application of a zero altered random forest model with two versions (ZAP_RF and ZAPC_RF) to deal with excess zeros in count response variables. The proposed model was compared with the conventional random forest (RF) model and with the conventional Generalized Poisson Ridge regression (GPR) using two real datasets, and we found that, in terms of prediction performance, the proposed zero inflated random forest model outperformed the conventional RF and GPR models.

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