Uncovering the Most Important Factors for Predicting Sexual Desire using Interpretable Machine Learning

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Sexual Desire ◽  
Learning Algorithm ◽  
Predictor Variable ◽  
Machine Learning Algorithm ◽  
Societal Factors ◽  
Interpretable Machine Learning ◽  
Several Variables ◽  
Two Samples

Previous studies have found a number of individual, relational, and societal factors that are associated with sexual desire. However, no studies to date have examined which of these variables are the most predictive of sexual desire. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict sexual desire from a large number of predictors across two samples (N = 1846; includes 754 individuals forming 377 couples). We also used a Shapley value technique to estimate the size and direction of the effect of each predictor variable on the model outcome. The model predicted around 40% of variance in dyadic and solitary desire with women’s desire being more predictable than men’s. Several variables consistently predicted sexual desire including individual, relational, and societal factors. The study provides the strongest evidence to date of the most important predictors for dyadic and solitary desire.

scholarly journals Physically interpretable machine learning algorithm on multidimensional non-linear fields

2021 ◽  
Vol 428 ◽  
pp. 110074
Author(s):  
Rem-Sophia Mouradi ◽  
Cédric Goeury ◽  
Olivier Thual ◽  
Fabrice Zaoui ◽  
Pablo Tassi
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Interpretable Machine Learning ◽  
Non Linear

scholarly journals Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study

2022 ◽  
Vol 12 (1) ◽  
pp. 114
Author(s):  
Chao Lu ◽  
Jiayin Song ◽  
Hui Li ◽  
Wenxing Yu ◽  
Yangquan Hao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
High Risk ◽  
Venous Thrombosis ◽  
Learning Algorithm ◽  
Population Based ◽  
Joint Disease ◽  
Recursive Feature Elimination ◽  
Machine Learning Algorithm ◽  
Interpretable Machine Learning

Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.

Detecting Cognitive Distraction Using Random Forest by Considering Eye Movement Type

2018 ◽  
pp. 1587-1599
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Random Forest ◽  
Decision Trees ◽  
Eye Movement ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Still Images ◽  
Cognitive Distraction ◽  
Movement Type

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

2021 ◽  
Author(s):  
Catherine Ollagnier ◽  
Claudia Kasper ◽  
Anna Wallenbeck ◽  
Linda Keeling ◽  
Siavash A Bigdeli
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Feeding Behaviour ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Tail Biting ◽  
Testing Set

Tail biting is a detrimental behaviour that impacts the welfare and health of pigs. Early detection of tail biting precursor signs allows for preventive measures to be taken, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (e.g., random forest, neural networks) were evaluated from daily feeding data collected from 65 pens originating from 2 herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. The random forest algorithm was able to predict 70% of the upcoming events with an accuracy of 94%, when predicting events in pens for which it had previous data. The detection of events for unknown pens was less sensitive, and the neural network model was able to detect 14% of the upcoming events with an accuracy of 63%. A machine-learning algorithm based on ongoing data collection should be considered for implementation into automatic feeder systems for real time prediction of tail biting events.

scholarly journals Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China

2021 ◽  
Vol 8 ◽  
Author(s):  
Xueyuan Huang ◽  
Yongjun Wang ◽  
Bingyu Chen ◽  
Yuanshuai Huang ◽  
Xinhua Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Random Forest ◽  
Blood Cells ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Kappa Coefficient ◽  
Gradient Boosting ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor

Background: Predicting the perioperative requirement for red blood cells (RBCs) transfusion in patients with the pelvic fracture may be challenging. In this study, we constructed a perioperative RBCs transfusion predictive model (ternary classifications) based on a machine learning algorithm.Materials and Methods: This study included perioperative adult patients with pelvic trauma hospitalized across six Chinese centers between September 2012 and June 2019. An extreme gradient boosting (XGBoost) algorithm was used to predict the need for perioperative RBCs transfusion, with data being split into training test (80%), which was subjected to 5-fold cross-validation, and test set (20%). The ability of the predictive transfusion model was compared with blood preparation based on surgeons' experience and other predictive models, including random forest, gradient boosting decision tree, K-nearest neighbor, logistic regression, and Gaussian naïve Bayes classifier models. Data of 33 patients from one of the hospitals were prospectively collected for model validation.Results: Among 510 patients, 192 (37.65%) have not received any perioperative RBCs transfusion, 127 (24.90%) received less-transfusion (RBCs < 4U), and 191 (37.45%) received more-transfusion (RBCs ≥ 4U). Machine learning-based transfusion predictive model produced the best performance with the accuracy of 83.34%, and Kappa coefficient of 0.7967 compared with other methods (blood preparation based on surgeons' experience with the accuracy of 65.94%, and Kappa coefficient of 0.5704; the random forest method with an accuracy of 82.35%, and Kappa coefficient of 0.7858; the gradient boosting decision tree with an accuracy of 79.41%, and Kappa coefficient of 0.7742; the K-nearest neighbor with an accuracy of 53.92%, and Kappa coefficient of 0.3341). In the prospective dataset, it also had a food performance with accuracy 81.82%.Conclusion: This multicenter retrospective cohort study described the construction of an accurate model that could predict perioperative RBCs transfusion in patients with pelvic fractures.

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