A hybrid Forecast Cost Benefit Classification of diabetes mellitus prevalence based on epidemiological study on Real-life patient’s data

Ren

et al. 2019

IJERPH

Self Cite

A chronic disease diabetes mellitus is assuming pestilence proportion worldwide. Therefore prevalence is important in all aspects. Researchers have introduced various methods, but still, the improvement is a need for classification techniques. This paper considers data mining approach and principal component analysis (PCA) techniques, on a single platform to approaches on the polytomous variable-based classification of diabetes mellitus and some selected chronic diseases. The PCA result shows eigenvalues, and the total variance is explained for the principal components (PCs) solution. Total of twelve attributes was analyzed with the intention to precise the pattern of the correlation with minimum factors as possible. Usually, factors with large eigenvalues retained. The first five components have their eigenvalues large enough to be retained. Their variances are 18.9%, 14.0%, 13.6%, 10.3%, and 8.6%, respectively. That explains ~65.3% of the total variance. We further applied K-means clustering with the aid of the first two PCs. As well, correlation results between diabetes mellitus and selected diseases; it has revealed that diabetes patients are more likely to have kidney and hypertension. Therefore, the study validates the proposed polytomous method for classification techniques. Such a study is important in better assessment on low socio-economic status zone regions around the globe.

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases

Ren

et al. 2019

IJERPH

Self Cite

“…In this study, the classification of patients diagnosed with diabetes mellitus (DM) was carried out in terms of social importance. That is to say, more than 425 million people worldwide were estimated to have DM [13], and the problem can cause other critical diseases [50].…”

Section: Data Collaboration For Medical Datamentioning

confidence: 99%

“…Medical big data is increasingly used for improving healthcare quality and clinical research, such as clinical decision support systems [3,5,44,50], identifying patients for clinical trials [36], and post-marketing surveillance of drugs [31,51]. While machine learning is one of the critical techniques for analyzing medical data [5,6,34,40,43,45], patients' privacy must be protected in the learning process.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes

et al. 2021

Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy. To overcome the trade-off, we apply the data collaboration analysis method to medical data. This method using artificial dummy data enables analysis to compare distributed information without using the original data. The purpose of our experiment is to identify patients diagnosed with diabetes mellitus (DM), using 29,802 instances of real data obtained from the University of Tsukuba Hospital between 01/03/2013 and 30/09/2018. The whole data is divided into a number of datasets to simulate different hospitals. We propose the following improvements for the data collaboration analysis. (1) Making the dummy data which has a reality and (2) using non-linear reconverting functions into the comparable space. Both can be realized using the generative adversarial network (GAN) and Node2Vec, respectively. The improvement effects of dummy data with GAN scores more than 10% over the effects of dummy data with random numbers. Furthermore, the improvement effect of the re-conversion by Node2Vec with GAN anchor data scores about 20% higher than the linear method with random dummy data. Our results reveal that the data collaboration method with appropriate modifications, depending on data type, improves analysis performance.

IFIP Advances in Information and Communication Technology

“…An effective way for maintenance of large OSS is the prediction of change-prone classes so that more resources may allocated to these classes. To assess the efficacy of the developed SCP models, we carried out cost-benefit analysis on all five datasets using HEL techniques [25]. The cost/benefit gain is computed as the saving of resources if the developed SCP models are put into use instead of random testing.…”

Section: Rq4 Does the Change In Base Learners Significantly Improve The Performance Of Models Developed By Hel?mentioning

confidence: 99%

Software Change Prediction with Homogeneous Ensemble Learners on Large Scale Open-Source Systems

Khanna

Shanmugapriya

Mehra

2021

Customizability, extensive community support and ease of availability have led to the popularity of Open-Source Software (OSS) systems. However, maintenance of these systems is a challenge especially as they become considerably large and complex with time. One possible method of ensuring effective quality in large scale OSS is the adoption of software change prediction models. These models aid in identifying change-prone parts in the early stages of software development, which can then be effectively managed by software practitioners. This study extensively evaluates eight Homogeneous Ensemble Learners (HEL) for developing software change prediction models on five large scale OSS datasets. HEL, which integrate the outputs of several learners of the same type are known to generate improved results than other non-ensemble classifiers. The study also statistically compares the results of the models developed by HEL with ten nonensemble classifiers. We further assess the change in performance of HEL for developing software change prediction models by substituting their default base learners with other classifiers. The results of the study support the use of HEL for developing software change prediction models and indicate Random Forest as the best HEL for the purpose.