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Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine

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JAYAPRAKASH JPINFOTECH
JAYAPRAKASH JPINFOTECH

Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine To buy this project in ONLINE, Contact: Email: jpinfotechprojects@gmail.com, Website: https://www.jpinfotech.org

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Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine ABSTRACT: The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patientā€™s follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset). In particular, among all the EHR features related to exemptions, examination and drug prescriptions we have selected only those collected before T2D diagnosis from a uniform age group of subjects. We demonstrated the reliability of the introduced approach with respect to other ML and Deep Learning approaches widely employed in the state-of-the-art for solving this task. Results evidence that the SB-SVM overcomes the other state- of-the-art competitors providing the best compromise between predictive performance and computation time. Additionally, the induced sparsity allows to increase the model interpretability, while implicitly managing high dimensional data and the usual unbalanced class distribution. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:
ļƒ˜ System : Pentium Dual Core. ļƒ˜ Hard Disk : 120 GB. ļƒ˜ Monitor : 15ā€™ā€™ LED ļƒ˜ Input Devices : Keyboard, Mouse ļƒ˜ Ram : 1 GB SOFTWARE REQUIREMENTS: ļƒ˜ Operating system : Windows 7. ļƒ˜ Coding Language : Python ļƒ˜ Database : MYSQL REFERENCE: Michele Bernardini, Luca Romeo, Paolo Misericordia, and Emanuele Frontoni, Senior Member, IEEE, ā€œDiscovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machineā€, IEEE Journal of Biomedical and Health Informatics, 2019.

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  • 1. Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine ABSTRACT: The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patientā€™s follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset). In particular, among all the EHR features related to exemptions, examination and drug prescriptions we have selected only those collected before T2D diagnosis from a uniform age group of subjects. We demonstrated the reliability of the introduced approach with respect to other ML and Deep Learning approaches widely employed in the state-of-the-art for solving this task. Results evidence that the SB-SVM overcomes the other state- of-the-art competitors providing the best compromise between predictive performance and computation time. Additionally, the induced sparsity allows to increase the model interpretability, while implicitly managing high dimensional data and the usual unbalanced class distribution. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:
  • 2. ļƒ˜ System : Pentium Dual Core. ļƒ˜ Hard Disk : 120 GB. ļƒ˜ Monitor : 15ā€™ā€™ LED ļƒ˜ Input Devices : Keyboard, Mouse ļƒ˜ Ram : 1 GB SOFTWARE REQUIREMENTS: ļƒ˜ Operating system : Windows 7. ļƒ˜ Coding Language : Python ļƒ˜ Database : MYSQL REFERENCE: Michele Bernardini, Luca Romeo, Paolo Misericordia, and Emanuele Frontoni, Senior Member, IEEE, ā€œDiscovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machineā€, IEEE Journal of Biomedical and Health Informatics, 2019.