A New Effective Machine Learning Framework for Sepsis Diagnosis

Wang, Xianchuan; Wang, Zhiyi; Weng, Jie; Wen, Charles H.-P.; Chen, Huiling; Wang, Xianqin

doi:10.1109/access.2018.2867728

Cited by 61 publications

(24 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has good robustness for noisy data and data with missing values and has a faster learning speed. Its variable importance measure can be used as a feature selection tool [30]. In recent years, it has been widely used in various classification, feature selection, and outlier detection problems.…”

Section: Methodsmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Under the background of "innovation and entrepreneurship," how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students' decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

show abstract

Section: Methodsmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the ICU scenario, automatic disease diagnosis prediction using the available clinical data can support clinicians in making quick decisions such that they can take further actions to save lives. In recent years, many researchers have worked on different methods [16], [20], [21] to predict different kinds of diseases, such as brain metastatic disease [13], heart disease [14] and sepsis [22]. Existing disease prediction methods can be roughly divided into two categories: clinical-based diagnosis [13], [21], [23] and data-based diagnosis [14], [16], [24], [25].…”

Section: Related Workmentioning

confidence: 99%

IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

et al. 2020

View full text Add to dashboard Cite

People are admitted to intensive care units (ICUs) because they need complete support for failing organ systems, constant monitoring, routine nursing care, and treatment. A critical or intensive illness is different from conventional or chronic diseases that most people are likely to have previously encountered. Such an illness is often unexpected and without warnings and can suddenly strike the previously fit. High levels of treatment and support are generally required to prevent life-threatening complications for the patents. Two of the most noticeable actions during an ICU stay are disease diagnosis and severity assessment of the patients. Unlike the majority of previous approaches where diagnosis and severity assessment are studied separately, we treat these actions as two tasks in an integrated procedure that clinicians must be able to quickly and accurately conduct such that patients are given the best possible chance for therapeutic success. In this paper, we propose an integrated disease diagnosis and severity assessment model (IDDSAM) to diagnose and assess diseases. Moreover, accompanying the prediction, we also provide an evidence-based explanation. IDDSAM is a multisource multitask model that is based on an attention mechanism and utilizes shareable information from laboratory tests, bedside monitoring, and complications to support patients' severity assessment and in-hospital disease diagnoses. We use 50,430 ICU cases consisting of 46,520 patients from 50 kinds of diseases over nine classifications to evaluate our proposed model. The experimental results demonstrated that our model outperforms the existing state-of-the-art mortality and diagnosis prediction framework by 3.79% on average in terms of accuracy for the mortality prediction tasks and by 14.51% on average for the diagnosis tasks.

show abstract

“…In the medical field, machine learning has already been used to detect human body status [14], analyze the relevant factors of the disease [15] and diagnose various diseases. For example, the models built by machine learning algorithms were used to diagnose heart disease [16], [17], diabetes and retinopathy [18], [19], acute kidney injury [20], [21], cancer [22] and other diseases [23], [24]. In these models, algorithms based on regression, tree, probability, decision surface and neural network were often effective.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

et al. 2020

View full text Add to dashboard Cite

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network) were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis. INDEX TERMS Chronic kidney disease, machine learning, KNN imputation, integrated model.

show abstract

A New Effective Machine Learning Framework for Sepsis Diagnosis

Cited by 61 publications

References 71 publications

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

Contact Info

Product

Resources

About