A method to predict diagnostic codes for chronic diseases using machine learning techniques

Gupta, Deepa; Khare, Sangita; Aggarwal, Ashish

doi:10.1109/ccaa.2016.7813730

Cited by 46 publications

(8 citation statements)

References 7 publications

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“…Ogunleye and Qing-Guo [38] Optimized XGBoost 100 Rady and Anwar [39] PNN 96.7 Gupta et al [40] AdaBoost 88.66 Khan et al [33] NBTree 98.75 Raju et al [42] XGBoost 99.29 Aljaaf et al [43] MLP 98.1 Our approach Improved SAE + Softmax 98…”

Section: Methods Accuracy (%)mentioning

confidence: 99%

“…In Table 6, we compare the proposed method with other recent CKD prediction research works, including an optimized XGBoost method [38], a probabilistic neural network (PNN) [39], and a method using adaptive boosting (AdaBoost) [40]. The other research works include a hybrid classifier of NB and decision tree (NBTree) [41], XGBoost [42], and a 7-7-1 MLP neural network [43].…”

Section: Methods Accuracy (%)mentioning

confidence: 99%

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Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

2020

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In recent times, several machine learning models have been built to aid in the prediction of diverse diseases and to minimize diagnostic errors made by clinicians. However, since most medical datasets seem to be imbalanced, conventional machine learning algorithms tend to underperform when trained with such data, especially in the prediction of the minority class. To address this challenge and proffer a robust model for the prediction of diseases, this paper introduces an approach that comprises of feature learning and classification stages that integrate an enhanced sparse autoencoder (SAE) and Softmax regression, respectively. In the SAE network, sparsity is achieved by penalizing the weights of the network, unlike conventional SAEs that penalize the activations within the hidden layers. For the classification task, the Softmax classifier is further optimized to achieve excellent performance. Hence, the proposed approach has the advantage of effective feature learning and robust classification performance. When employed for the prediction of three diseases, the proposed method obtained test accuracies of 98%, 97%, and 91% for chronic kidney disease, cervical cancer, and heart disease, respectively, which shows superior performance compared to other machine learning algorithms. The proposed approach also achieves comparable performance with other methods available in the recent literature.

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Section: Methods Accuracy (%)mentioning

confidence: 99%

Section: Methods Accuracy (%)mentioning

confidence: 99%

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

2020

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“…A noteworthy research work has been conducted which discusses the summarization of several chronic diseases using machine learning techniques which include diabetes disease also [18]. Sarwar et al [41] performed a predictive analysis and revealed which algorithm is best suited for predicting diabetes disease.…”

Section: Exploration Of Diabetes Diseasementioning

confidence: 99%

DiaVis: Exploration and Analysis of Diabetes through Visual Interactive System

Rahman¹,

Islam²,

Akter³

et al. 2021

HCIS

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Background: Diabetes is a long-term disease characterized by high blood sugar and has risen as a public health problem globally. Exploring and analyzing diabetes data is a timely concern because it may prompt a variety of serious illnesses, including stroke, kidney failure, heart attacks, etc. Several existing pieces of research have revealed that diabetes data, such as systolic blood pressure (SBP), diastolic blood pressure (DBP), weight, height, age, etc., can provide insightful information about patients diabetes states. However, very few studies have focused on visualizing diabetes mellitus (DM) insights to support healthcare administrator (HA)'s goals adequately, such as (i) decision-making, (ii) identifying and grouping associated factors, and (iii) analyzing large data effectively remains unexplored.Objective: This study aims to design an interactive visual system (Vis) to explore diabetes mellitus (DM) insights and its associated factors in Bangladesh. Methods:In this study, first, a case study method has employed to understand diabetes data. Second, we examine the potential of user-centered technology in addressing these challenges and design a Vis named "DiaVis" to process and present raw data in the form of graphics, graphs, and processed text, as well as a variety of user interaction possibilities. It helps to extract valuable data and present it in a simple and easy-to-understand way. Moreover, we highlight some key insights from our study that may help explore the healthcare community.Results: A user study with 20 individuals is used to evaluate our system. By allowing iterative exploration and modification of data in a dashboard with multiple-coordinated views, the DiaVis system improves the flow of visual analysis. Conclusion:This study suggests that the healthcare community should pay more attention to developing appropriate policy measures to reduce the risk of DM.

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“…This technology can achieve accurate and economical diagnoses of diseases; hence, it might be a promising method for diagnosing CKD. It has become a new kind of medical tool with the development of information technology [12] and has a broad application prospect because of the rapid development of electronic health record [13]. 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.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

et al. 2020

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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.

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A method to predict diagnostic codes for chronic diseases using machine learning techniques

Cited by 46 publications

References 7 publications

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

DiaVis: Exploration and Analysis of Diabetes through Visual Interactive System

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

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