Partially Synthesised Dataset to Improve Prediction Accuracy (Case Study: Prediction of Heart Diseases)

The real world data sources, such as statistical agencies, library data-banks and research institutes are the major data sources for researchers. Using this type of data involves several advantages including, the improvement of credibility and validity of the experiment and more importantly, it is related to a real world problems and typically unbiased. However, this type of data is most likely unavailable or inaccessible for everyone due to the following reasons. First, privacy and confidentiality concerns, since the data must to be protected on legal and ethical basis. Second, collecting real world data is costly and time consuming. Third, the data may be unavailable, particularly in the newly arises research subjects. Therefore, many studies have attributed the use of fully and/or partially synthesised data instead of real world data due to simplicity of creation, requires a relatively small amount of time and sufficient quantity can be generated to fit the requirements. In this context, this study introduces the use of partially synthesised data to improve the prediction of heart diseases from risk factors. We are proposing the generation of partially synthetic data from agreed principles using rule-based method, in which an extra risk factor will be added to the real-world data. In the conducted experiment, more than 85% of the data was derived from observed values (i.e., real-world data), while the remaining data has been synthetically generated using a rule-based method and in accordance with the World Health Organisation criteria. The analysis revealed an improvement of the variance in the data using the first two principal components of partially synthesised data. A further evaluation has been con-ducted using five popular supervised machine-learning classifiers. In which, partially synthesised data considerably improves the prediction of heart diseases. Where the majority of classifiers have approximately doubled their predictive performance using an extra risk factor

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy

Gamification in e-governance: Development of an online gamified system to enhance government entities services delivery and promote public's awareness

© 2017 ACM.Electronic Governance (e-Governance) is the application of the Information and Communication Technology (ICT) with the aim to simplify and support the governance across different parties including public government organizations, business and citizens. Through the adoption and use of Information and Communication technology which will connect all of these three together to support the overall government's processes and operations. It's anticipated that eGovernance shall bring boundless improvements towards strategic planning, proper monitoring of government programs, investments, projects and activities. The eGovernance will provide easy access and delivery of government services to the citizens and reduce associated costs of transactions that occur across government entities. In the recent years, some of the new technological advancement concepts that include Gamification becomes one of the solutions that can be attached with the e-Governance implementation to sustain the effective adoption of government services delivery. Gamification is an evolution that supports people interactions with implemented government electronic services. It can be widely used within public organizations for training of new hires at workplaces, help employees to perform certain tasks and carry their dayto-day activities more efficiently by using Gamification tools which government entities has to offer in order to facilitate eGovernance implementation and services adoption by publics. The developed mobile application is based on a Gamification platform for employees at public government organizations for the purpose of training and learning. In this research, different variables were measured including productivity, motivational engagement, performance, training, support and services, collaboration, innovation, skills development, personal development and behavior changes

A Data Science and Machine Learning Approach to Measure and Monitor Physical Activity in Children

Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments. The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases

Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics

© 2018 IEEE. Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond

Patients Attitude to Technology: A Way to Improve Hydrocephalus Management and Follow up Using Smartphone Intelligent Application

Smartphone applications (”apps”) have become ubiquitous with the advent of smartphones and tablets in recent years.Increasingly the utility of these apps is being explored in healthcare delivery. Hydrocephalus is a condition that is usually followed by a neurosurgeon for the patient’s life. We explore patient acceptability of a mobile app as an adjunct to outpatient follow-up of patients with hydrocephalus. A questionnaire was circulated amongst patients with hydrocephalus (adults and children). Patients were asked questions about their hydrocephalus; expectations for outpatient follow up, whether they have smartphone/tablet/internet access and whether they would be interested in a mobile app for their long term hydrocephalus follow up. 191 patients completed questionnaires, 98 respondents were adults (mean age 46.1) and 93 were children less than 18 years old (mean age 8). Overall 36.1% of patients did not know the cause of their hydrocephalus. 96.7% have a shunt. 76.5% of adults and 80.6% of children had 1-4 shunt surgeries, 14.3% of adults and 11.8% of children had 5-9 shunt surgeries, 3.1% of adults and 5.4% of children had 10-14 shunt surgeries. 71.7% of patients expect to be followedup routinely in clinic for life. All children had smartphones or tablets, compared to 86.7% of adults. Children were more interested in a hydrocephalus app, 84.9% saying yes, compared to 71.4% of adults. Adults who were not interested in the app did not have a smartphone or tablet. Hydrocephalus management is a lifelong task and innovations in technology for engaging patients in its management are vital. The majority of patients are interested in mobile apps for outpatient management of hydrocephalus. We will follow this up with a feasibility study of a custom designed hydrocephalus app

Gamification in e-Governance: Development of an online gamified system to enhance government entities services delivery and promote public's awareness

Electronic Governance (e-Governance) is the application of the Information and Communication Technology (ICT) with the aim to simplify and support the governance across different parties including public government organizations, business and citizens. Through the adoption and use of Information and Communication technology which will connect all of these three together to support the overall government's processes and operations. It's anticipated that e-Governance shall bring boundless improvements towards strategic planning, proper monitoring of government programs, investments, projects and activities. The e-Governance will provide easy access and delivery of government services to the citizens and reduce associated costs of transactions that occur across government entities. In the recent years, some of the new technological advancement concepts that include Gamification becomes one of the solutions that can be attached with the e-Governance implementation to sustain the effective adoption of government services delivery. Gamification is an evolution that supports people interactions with implemented government electronic services. It can be widely used within public organizations for training of new hires at workplaces, help employees to perform certain tasks and carry their day-to-day activities more efficiently by using Gamification tools which government entities has to offer in order to facilitate e-Governance implementation and services adoption by publics. The developed mobile application is based on a Gamification platform for employees at public government organizations for the purpose of training and learning. In this research, different variables were measured including productivity, motivational engagement, performance, training, support and services, collaboration, innovation, skills development, personal development and behavior changes

Evaluation of Machine Learning Methods to Predict Knee Loading from the Movement of Body Segments

Abnormal joint moments during gait are validated predictors of knee pain in osteoarthritis. Calculation of moments necessitates measurement of forces and moment arms about joints during walking. Dynamically changing moment arms can be calculated from motion trackers either optically or with wireless inertia sensing units, but the measurement of forces is more problematic. Either the patient has to walk over a force platform or a force sensing device has to be built into the sole of the shoes. One possible means of registering abnormal join moments without the restrictions due to force measurements is to predict moments from the movement of body segments using advanced machine learning techniques. To test the viability of this approach, we aimed to predict the frontal plane internal knee abduction moment form 3D Euler angles of the ankle, knee, hip and pelvis during a single gait cycle of 31 patients with alkaptonuria. Four machine learning algorithms were used in our experiment to predict moments namely: Decision Tree, Random Forest, Linear Regression and Multilayer Perceptron neural network. Based on performance measures of prediction (R2, root mean squared error and area under the recall curve), the random forest algorithm performed best but this was also the slowest by a factor of 10. Considering both performance and speed, the Multilayer Perceptron neural network method was superior with R2, root mean square of error, area under the recall curve and required training time of 0.8616, 0.0743, 0.874 and 730 ms, respectively

Gamification in e-governance: Development of an online gamified system to enhance government entities services delivery and promote public's awareness

© 2017 ACM.Electronic Governance (e-Governance) is the application of the Information and Communication Technology (ICT) with the aim to simplify and support the governance across different parties including public government organizations, business and citizens. Through the adoption and use of Information and Communication technology which will connect all of these three together to support the overall government's processes and operations. It's anticipated that eGovernance shall bring boundless improvements towards strategic planning, proper monitoring of government programs, investments, projects and activities. The eGovernance will provide easy access and delivery of government services to the citizens and reduce associated costs of transactions that occur across government entities. In the recent years, some of the new technological advancement concepts that include Gamification becomes one of the solutions that can be attached with the e-Governance implementation to sustain the effective adoption of government services delivery. Gamification is an evolution that supports people interactions with implemented government electronic services. It can be widely used within public organizations for training of new hires at workplaces, help employees to perform certain tasks and carry their dayto-day activities more efficiently by using Gamification tools which government entities has to offer in order to facilitate eGovernance implementation and services adoption by publics. The developed mobile application is based on a Gamification platform for employees at public government organizations for the purpose of training and learning. In this research, different variables were measured including productivity, motivational engagement, performance, training, support and services, collaboration, innovation, skills development, personal development and behavior changes