Dataset Information

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

ABSTRACT:

Background

Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided.

Methods

This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance.

Results

The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

SUBMITTER: Kuo KM

PROVIDER: S-EPMC7487151 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

Kuo Kuang-Ming KM Talley Paul P Kao YuHsi Y Huang Chi Hsien CH

PeerJ 20200910

<h4>Background</h4>Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a mu ...[more]

PMID: 32974105

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Project description:Necrotising fasciitis is insidiously advancing soft tissue infection, characterized by widespread fascial necrosis. One of the most common conditions encountered in the clinical practice and which required clinical suspicion in patients who have less resistance to these infections. Especially in diabetic patients who are more prone for such infection due to low immunity and other reasons like early detection of these infections helps these patients to have a better recovery by aggressive surgical management and other measures required for the same. Literature has enough evidence on how serious this entity. Hence this study is designed in that aspect to help in early diagnosis and appropriate treatment. This study will help to determine the clinical presentation, the polymicrobial involved. The organisms most closely linked to necrotizing fasciitis are group A beta-hemolytic streptococci, although the disease may also be caused by other bacteria and their sensitivity patterns will help us to install appropriate antibiotics with the surgical management. And to compare our mortality rate with the other studies. A prospective descriptive study including all type II diabetic patients with necrotizing fasciitis over 3 years. Patients were evaluated in detail with respect to patient's history, examination, culture sensitivity and chart reviews and followed up using standard evaluation tools to measure the various outcomes. In 62 patients, the commonest clinical presentation was necrotic patch of skin and cellulites. The organisms cultured being beta hemolytic streptococci and E.Coli. The culture sensitivity pattern is to broad-spectrum antibiotics. The average stay in the hospital was about 13.74 days for the study group and the time delay in presentation is 6 days. Wagner's score is a useful tool for aiding the diagnosis. Patients without foot care had extensive lesions. And a mortality rate of 1.6 % in these patients. In an acutely swollen limb in a diabetic patient a high degree of suspicion of necrotizing fasciitis is required. A detailed foot care is warranted in these patients. An early extensive debridement is mandatory and key for a favorable outcome. And to start on broad- spectrum antibiotics is advisable. Wagner's scoring is helpful to predict the chances of a second surgery. With the above entities in mind the mortality rate of necrotizing fasciitis in type ii diabetes mellitus can be contained.

Dataset Information

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

Background

Methods

Results

Publications

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

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