Geospatial infrastructure solutions for local government sectors: geo-enabling infectious disease monitoring system in state health departments

Abdul, Rasam Abdul Rauf; Ahmad, Zuki Nur Syaidatul Syuhada; Nurhafiza, Saad; Ghazali, Rosmadi

doi:10.19101/ijatee.2020.762193

IJATEE

2021

DOI: 10.19101/ijatee.2020.762193

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Geospatial infrastructure solutions for local government sectors: geo-enabling infectious disease monitoring system in state health departments

Rasam Abdul Rauf Abdul¹,

Zuki Nur Syaidatul Syuhada Ahmad²,

Saad Nurhafiza³

et al.

Abstract: Tuberculosis (TB) disease is one of the most common diseases among Selangor citizens, Malaysia, due to the high population density, particularly in urban areas and low socioeconomic strata. TB's death rate was nine times higher in Selangor last year, according to the statistics from the Selangor Health Department [1]. There were 3423 cases of TB recorded in 2012 and 5,071 cases of new TB cases recorded in Selangor in

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Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

Dubey¹,

Gupta²,

Jain³

2022

Computers, Materials &Amp; Continua

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This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization. Five benchmark datasets were considered from the University of California, Irvine (UCI) Machine Learning Repository for comparative study and experimentation. These are breast cancer Wisconsin (BCW), Pima Indians Diabetes, Heart-Statlog, Hepatitis, and Cleveland Heart Disease datasets. The combined average accuracy obtained collectively is approximately 99.4% in case of TLBO-KM and 98.6% in case of TLBO-FCM. This approach is also capable of finding the dominating attributes. The findings indicate that TLBO-KM/FCM, considering different computational measures, perform well on the non-clustered data where k-means and FCM, if employed independently, fail to provide significant results. Evaluating different feature sets, the TLBO-KM/FCM and SVM(GS) clearly outperformed all other classifiers in terms of sensitivity, specificity and accuracy. TLBO-KM/FCM attained the highest average sensitivity (98.7%), highest average specificity (98.4%) and highest average accuracy (99.4%) for 10-fold cross validation with different test data.

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Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

Dubey¹,

Gupta²,

Jain³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

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Geospatial infrastructure solutions for local government sectors: geo-enabling infectious disease monitoring system in state health departments

Cited by 1 publication

References 15 publications

Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

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