scholarly journals Predicting Consumer Purchasing Decisions in the Online Food Delivery Industry

2021 ◽  
Author(s):  
Batool Madani ◽  
Hussam Alshraideh
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Prediction Models ◽  
Food Delivery ◽  
Purchasing Decisions ◽  
Class Label ◽  
Bottom Line ◽  
Rule Based ◽  
Online Platforms ◽  
Regression Algorithms

This transformation of food delivery businesses to online platforms has gained high attention in recent years. This due to the availability of customizing ordering experiences, easy payment methods, fast delivery, and others. The competition between online food delivery providers has intensified to attain a wider range of customers. Hence, they should have a better understanding of their customers’ needs and predict their purchasing decisions. Machine learning has a significant impact on companies’ bottom line. They are used to construct models and strategies in industries that rely on big data and need a system to evaluate it fast and effectively. Predictive modeling is a type of machine learning that uses various regression algorithms, analytics, and statistics to estimate the probability of an occurrence. The incorporation of predictive models helpsonline food delivery providers to understand their customers. In this study, a dataset collected from 388 consumers in Bangalore, India was provided to predict their purchasing decisions. Four prediction models are considered: CART and C4.5 decision trees, random forest, and rule-based classifiers, and their accuracies in prodividing the correct class label are evaluated. The findings show that all models perform similarly, but the C4.5 outperforms them all with an accuracy of 91.67%.

scholarly journals Enhancing Generalizability of Predictive Models with Synergy of Data and Physics

2021 ◽  
Author(s):  
Yingjun Shen ◽  
Zhe Song ◽  
Andrew Kusiak
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Predictive Models ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Divide And Conquer ◽  
Less Is More ◽  
Industrial Big Data

Abstract Wind farm needs prediction models for predictive maintenance. There is a need to predict values of non-observable parameters beyond ranges reflected in available data. A prediction model developed for one machine many not perform well in another similar machine. This is usually due to lack of generalizability of data-driven models. To increase generalizability of predictive models, this research integrates the data mining with first-principle knowledge. Physics-based principles are combined with machine learning algorithms through feature engineering, strong rules and divide-and-conquer. The proposed synergy concept is illustrated with the wind turbine blade icing prediction and achieves significant prediction accuracy across different turbines. The proposed process is widely accepted by wind energy predictive maintenance practitioners because of its simplicity and efficiency. Furthermore, the testing scores of KNN, CART and DNN algorithm are increased by 44.78%, 32.72% and 9.13% with our proposed process. We demonstrated the importance of embedding physical principles within the machine learning process, and also highlight an important point that the need for more complex machine learning algorithms in industrial big data mining is often much less than it is in other applications, making it essential to incorporate physics and follow “Less is More” philosophy.

scholarly journals Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases

2021 ◽  
Vol 1 ◽  
Author(s):  
Attayeb Mohsen ◽  
Lokesh P. Tripathi ◽  
Kenji Mizuguchi
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Adverse Drug Reactions ◽  
Predictive Models ◽  
Prediction Models ◽  
Expression Profiles ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Wide Range

Machine learning techniques are being increasingly used in the analysis of clinical and omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) and the build-up of health-related big data. In this paper we have aimed at estimating the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery using various machine learning methods. We have also described a novel machine learning-based framework for predicting the likelihood of ADRs. Our framework combines two distinct datasets, drug-induced gene expression profiles from Open TG–GATEs (Toxicogenomics Project–Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs. It incorporates data filtering and cleaning as well as feature selection and hyperparameters fine tuning. Using this framework with Deep Neural Networks (DNN), we built a total of 14 predictive models with a mean validation accuracy of 89.4%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As case studies, we have investigated the performances of our prediction models in the context of Duodenal ulcer and Hepatitis fulminant, highlighting mechanistic insights into those ADRs. We have generated predictive models to help to assess the likelihood of ADRs in testing novel pharmaceutical compounds. We believe that our findings offer a promising approach for ADR prediction and will be useful for researchers in drug discovery.

Construction and Comparison of Predictive Models for Length of Stay after Total Knee Arthroplasty: Regression Model and Machine Learning Analysis Based on 1,826 Cases in a Single Singapore Center

2020 ◽  
Author(s):  
Hui Li ◽  
Juyang Jiao ◽  
Shutao Zhang ◽  
Haozheng Tang ◽  
Xinhua Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Knee Arthroplasty ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Knee Arthroplasty ◽  
Predictive Models ◽  
Prediction Models ◽  
Univariate Analysis ◽  
Machine Learning Algorithms ◽  
Total Knee

AbstractThe purpose of this study was to develop a predictive model for length of stay (LOS) after total knee arthroplasty (TKA). Between 2013 and 2014, 1,826 patients who underwent TKA from a single Singapore center were enrolled in the study after qualification. Demographics of patients with normal and prolonged LOS were analyzed. The risk variables that could affect LOS were identified by univariate analysis. Predictive models for LOS after TKA by logistic regression or machine learning were constructed and compared. The univariate analysis showed that age, American Society of Anesthesiologist level, diabetes, ischemic heart disease, congestive heart failure, general anesthesia, and operation duration were risk factors that could affect LOS (p < 0.05). Comparing with logistic regression models, the machine learning model with all variables was the best model to predict LOS after TKA, of whose area of operator characteristic curve was 0.738. Machine learning algorithms improved the predictive performance of LOS prediction models for TKA patients.

scholarly journals Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

BMJ ◽  
2020 ◽  
pp. m958 ◽  
Author(s):  
Elham Mahmoudi ◽  
Neil Kamdar ◽  
Noa Kim ◽  
Gabriella Gonzales ◽  
Karandeep Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Predictive Models ◽  
Hospital Readmission ◽  
Prediction Models ◽  
Cox Regression ◽  
Patient Specific ◽  
Day Hospital ◽  
Significant Difference

Abstract Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

scholarly journals Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habeeb Balogun ◽  
Hafiz Alaka ◽  
Christian Nnaemeka Egwim
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Predictive Models ◽  
Data Analytics ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Support Vector ◽  
Traffic Data ◽  
Content Type ◽  
Pollution Concentration

PurposeThis paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.Design/methodology/approachThis research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.FindingsThe results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution.Practical implicationsThis paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system.Originality/valueThis research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration.

Discovering the Predictive Value of Clinical Notes: Machine Learning Analysis with Text Representation

2020 ◽  
Vol 10 (12) ◽  
pp. 2869-2875
Author(s):  
Kareen Teo ◽  
Ching Wai Yong ◽  
Joon Huang Chuah ◽  
Belinda Pingguan Murphy ◽  
Khin Wee Lai
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Predictive Models ◽  
Prediction Models ◽  
Feature Engineering ◽  
Free Text ◽  
Learning To Learn ◽  
Healthcare Facilities ◽  
Discrimination Ability ◽  
Clinical Notes

Hospital readmission shortly after discharge is threatening to plague the quality of inpatient care. Readmission is a severe episode that leads to increased medical care costs. Federal regulations and early readmission penalties have created an incentive for healthcare facilities to reduce their readmission rates by predicting patients at a high risk of readmission. Scientists have developed prediction models by using rule-based assessment scores and traditional statistical methods, and most have focused on structured patient records. Recently, a few researchers utilized unstructured clinical notes. However, they achieved moderate prediction accuracy by making predictions of a single diagnosis subpopulation via extensive feature engineering. This study proposes the use of machine learning to learn deep representation of patient notes for the identification of high-risk readmission in a hospital-wide population. We describe and train several predictive models (standard machine learning and neural network), to which several setups have not been applied. Results show that complex deep learning models significantly outperform (P < 0.001) conventionally applied simple models in terms of discrimination ability. We also demonstrate a simple feature evaluation using a standard model, which allows the determination of potential clinical conditions/procedures for targeting. Unlike modeling using structured patient information with considerable variability in structure when different templates or databases are adopted, this study shows that the machine learning approach can be applied to prognosticate readmission with clinical free text in various healthcare settings. Using minimum feature engineering, the trained models perform comparably well or better than other predictive models established in previous literature.

Comprehensible Explanation of Predictive Models

2019 ◽  
pp. 612-623
Author(s):  
Marko Robnik-Šikonja
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Predictive Models ◽  
Decision Process ◽  
Classification Accuracy ◽  
Prediction Models ◽  
Classification Models ◽  
Successful Prediction ◽  
Applications Of Machine Learning ◽  
General Explanation

The most successful prediction models (e.g., SVM, neural networks, or boosting) unfortunately do not provide explanations of their predictions. In many important applications of machine learning, the comprehension of the decision process is of utmost importance and dominates the classification accuracy (e.g., in business and medicine). This chapter introduces general explanation methods that are independent of the prediction model and can be used with all classification models that output probabilities. It explains how the methods work and graphically explains models' decisions for new unlabeled cases. The approach is put in the context of applications from medicine, business, and macro-economy.

scholarly journals Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1810
Author(s):  
Sameera Senanayake ◽  
Adrian Barnett ◽  
Nicholas Graves ◽  
Helen Healy ◽  
Keshwar Baboolal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kidney Transplant ◽  
Predictive Models ◽  
Graft Failure ◽  
Prediction Models ◽  
Deceased Donor ◽  
Machine Learning Techniques ◽  
Kidney Graft ◽  
Learning Techniques ◽  
Deceased Donor Kidney Transplant

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model’s performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

scholarly journals Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1810 ◽  
Author(s):  
Sameera Senanayake ◽  
Adrian Barnett ◽  
Nicholas Graves ◽  
Helen Healy ◽  
Keshwar Baboolal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kidney Transplant ◽  
Predictive Models ◽  
Graft Failure ◽  
Prediction Models ◽  
Deceased Donor ◽  
Machine Learning Techniques ◽  
Kidney Graft ◽  
Learning Techniques ◽  
Deceased Donor Kidney Transplant

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1st, 2007, to December 31st, 2017.  This included 3,758 live donor transplants and 7,365 deceased donor transplants.  Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models.  The best predictive model will be selected based on the model’s performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia.   Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques.  Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

Comprehensible Explanation of Predictive Models

2018 ◽  
pp. 2085-2094
Author(s):  
Marko Robnik-Šikonja
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Predictive Models ◽  
Decision Process ◽  
Classification Accuracy ◽  
Prediction Models ◽  
Classification Models ◽  
Successful Prediction ◽  
Applications Of Machine Learning ◽  
General Explanation

The most successful prediction models (e.g., SVM, neural networks, or boosting) unfortunately do not provide explanations of their predictions. In many important applications of machine learning the comprehension of the decision process is of uttermost importance and dominates the classification accuracy, e.g., in business and medicine. This chapter introduces general explanation methods that are independent of the prediction model and can be used with all classification models that output probabilities. It explains how the methods work and graphically explains models' decisions for new unlabelled cases. The approach is put in the context of applications from medicine, business and macro economy.

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