scholarly journals Machine Learning Methods for Diabetes Prediction

2019 ◽  
Vol 8 (12) ◽  
pp. 2199-2205
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Research Field ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Health Practitioners ◽  
Prediction Technique ◽  
Diabetes Prediction ◽  
Machine Learning Models

Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor

scholarly journals Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation (Preprint)

2019 ◽  
Author(s):  
Tianzhou Yang ◽  
Li Zhang ◽  
Liwei Yi ◽  
Huawei Feng ◽  
Shimeng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Ensemble Methods ◽  
Diabetes Screening ◽  
Linear Discriminant ◽  
Machine Learning Methods ◽  
Diabetes Prediction ◽  
Validation Set ◽  
Fold Cross Validation

BACKGROUND Early diabetes screening can effectively reduce the burden of disease. However, natural population–based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes. OBJECTIVE The aim of this study was to build prediction models based on the ensemble learning method for diabetes screening to further improve the health status of the population in a noninvasive and inexpensive manner. METHODS The dataset for building and evaluating the diabetes prediction model was extracted from the National Health and Nutrition Examination Survey from 2011-2016. After data cleaning and feature selection, the dataset was split into a training set (80%, 2011-2014), test set (20%, 2011-2014) and validation set (2015-2016). Three simple machine learning methods (linear discriminant analysis, support vector machine, and random forest) and easy ensemble methods were used to build diabetes prediction models. The performance of the models was evaluated through 5-fold cross-validation and external validation. The Delong test (2-sided) was used to test the performance differences between the models. RESULTS We selected 8057 observations and 12 attributes from the database. In the 5-fold cross-validation, the three simple methods yielded highly predictive performance models with areas under the curve (AUCs) over 0.800, wherein the ensemble methods significantly outperformed the simple methods. When we evaluated the models in the test set and validation set, the same trends were observed. The ensemble model of linear discriminant analysis yielded the best performance, with an AUC of 0.849, an accuracy of 0.730, a sensitivity of 0.819, and a specificity of 0.709 in the validation set. CONCLUSIONS This study indicates that efficient screening using machine learning methods with noninvasive tests can be applied to a large population and achieve the objective of secondary prevention.

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia

2019 ◽  
pp. 1-11 ◽  
Author(s):  
David Chen ◽  
Gaurav Goyal ◽  
Ronald S. Go ◽  
Sameer A. Parikh ◽  
Che G. Ngufor
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Unsupervised Clustering ◽  
Support Vector ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Continuous Output ◽  
Time To First Treatment ◽  
Machine Learning Models

PURPOSE Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can readily learn complex nonlinear relationships, many methods are criticized as inadequate because of limited interpretability. We propose using unsupervised clustering of the continuous output of machine learning models to provide discrete risk stratification for predicting time to first treatment in a cohort of patients with CLL. PATIENTS AND METHODS A total of 737 treatment-naïve patients with CLL diagnosed at Mayo Clinic were included in this study. We compared predictive abilities for two survival models (Cox proportional hazards and random survival forest) and four classification methods (logistic regression, support vector machines, random forest, and gradient boosting machine). Probability of treatment was then stratified. RESULTS Machine learning methods did not yield significantly more accurate predictions of time to first treatment. However, automated risk stratification provided by clustering was able to better differentiate patients who were at risk for treatment within 1 year than models developed using standard survival analysis techniques. CONCLUSION Clustering the posterior probabilities of machine learning models provides a way to better interpret machine learning models.

scholarly journals Identification of significant climatic risk factors and machine learning models in dengue outbreak prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia. Results This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks. Conclusions This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

scholarly journals Empirical asset pricing via machine learning: evidence from the European stock market

2021 ◽  
Author(s):  
Wolfgang Drobetz ◽  
Tizian Otto
Keyword(s):  
Machine Learning ◽  
Stock Returns ◽  
Network Architecture ◽  
Risk Measures ◽  
Predictive Performance ◽  
Support Vector ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Models

AbstractThis paper evaluates the predictive performance of machine learning methods in forecasting European stock returns. Compared to a linear benchmark model, interactions and nonlinear effects help improve the predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality problem and to avoid overfitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit substantial variation in statistical predictive performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce sizeable gains relative to our benchmark. Neural networks perform best, also after accounting for transaction costs. A classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2020 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

scholarly journals Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation

10.2196/15431 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e15431
Author(s):  
Tianzhou Yang ◽  
Li Zhang ◽  
Liwei Yi ◽  
Huawei Feng ◽  
Shimeng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Ensemble Methods ◽  
Diabetes Screening ◽  
Linear Discriminant ◽  
Machine Learning Methods ◽  
Diabetes Prediction ◽  
Validation Set ◽  
Fold Cross Validation

Background Early diabetes screening can effectively reduce the burden of disease. However, natural population–based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes. Objective The aim of this study was to build prediction models based on the ensemble learning method for diabetes screening to further improve the health status of the population in a noninvasive and inexpensive manner. Methods The dataset for building and evaluating the diabetes prediction model was extracted from the National Health and Nutrition Examination Survey from 2011-2016. After data cleaning and feature selection, the dataset was split into a training set (80%, 2011-2014), test set (20%, 2011-2014) and validation set (2015-2016). Three simple machine learning methods (linear discriminant analysis, support vector machine, and random forest) and easy ensemble methods were used to build diabetes prediction models. The performance of the models was evaluated through 5-fold cross-validation and external validation. The Delong test (2-sided) was used to test the performance differences between the models. Results We selected 8057 observations and 12 attributes from the database. In the 5-fold cross-validation, the three simple methods yielded highly predictive performance models with areas under the curve (AUCs) over 0.800, wherein the ensemble methods significantly outperformed the simple methods. When we evaluated the models in the test set and validation set, the same trends were observed. The ensemble model of linear discriminant analysis yielded the best performance, with an AUC of 0.849, an accuracy of 0.730, a sensitivity of 0.819, and a specificity of 0.709 in the validation set. Conclusions This study indicates that efficient screening using machine learning methods with noninvasive tests can be applied to a large population and achieve the objective of secondary prevention.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2019 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
World Health ◽  
Support Vector ◽  
Strong Impact ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Depth Analysis ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s main pandemic vector-borne infections and serious hazard to humanity. According to the World Health Organization (WHO), the incidence of dengue has grown dramatically worldwide in recent decades. The WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. Until today there is no tested vaccine or treatment to stop or prevent dengue fever thus the importance of dengue outbreak prediction is significant. The current issue in dengue outbreak prediction is accuracy. There are a limited number of studies that look at in depth analysis of climate factors in dengue outbreak prediction. Methods: In this study, the most significant and important climatic factors that contribute to dengue outbreak were identified. These factors were used as input parameters on machine learning models. The models were trained and evaluated based on four-year data from January 2010 to December 2013 in Malaysia. Results: This work provides two main contributions. A new risk factor, which was called TempeRain Factor (TRF), was determined and used as an input parameter for dengue prediction outbreak model. Moreover, the TRF was applied to demonstrate that its strong impact on dengue outbreaks. Experimental results showed that Support Vector Machine (SVM) with the newly identified meteorological risk factor in this study resulted in higher accuracy of 98.09% and reduced the root mean square error to 0.098 for predicting dengue outbreak. Conclusions: This research managed to explore on the factors that are being used in dengue outbreak prediction systems. The main contribution of this paper is in identifying new significant factors that contribute in dengue outbreak prediction. From the evaluation, we managed to obtain a significant improvement in accuracy of the machine-learning model in dengue outbreak prediction.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2020 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

scholarly journals Determining hypertensive patients’ beliefs towards medication and associations with medication adherence using machine learning methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8286
Author(s):  
Firdaus Aziz ◽  
Sorayya Malek ◽  
Adliah Mhd Ali ◽  
Mee Sieng Wong ◽  
Mogeeb Mosleh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medication Adherence ◽  
Performance Measure ◽  
Variable Importance ◽  
Support Vector ◽  
Learning Models ◽  
Learning Methods ◽  
Hypertensive Patients ◽  
Machine Learning Methods ◽  
Machine Learning Models

Background This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients’ adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients’ adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature. Methods Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients’ adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients’ adherence levels and variables were generated using SOM. Result Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern. Conclusion This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients’ adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

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