A belief rule-based expert system to assess mental disorder under uncertainty

Hossain, Mohammad Shahadat; Monrat, Ahmed Afif; Karim, Razuan; Bhuiyan, Tanveer Ahmed; Khalid, Md. Saifuddin

doi:10.1109/iciev.2016.7760167

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…In the future, we will however obtain the most current data from different regions worldwide for the treatment of liver disorders and the prevention of liver disease. We will also apply some methodology which can eliminate the uncertainty like Belief-Rule Based (BRB) [24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Resultsmentioning

confidence: 99%

A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

Nahar

Ara

Neloy

et al. 2019

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET)

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Early diagnosis of liver disease is very important in order to save human lives and take appropriate measure to control the disease. In several fields, especially in the field of medical science, the ensemble method was successfully applied. This research work uses different ensemble methods to investigate the early detection of liver disease. The selected dataset for this analysis is made up of attributes such as total bilirubin, direct bilirubin, age, sex, total protein, albumin, and globulin ratio. This research mainly aims at measuring and comparing the efficiency of different ensemblemethods. AdaBoost, LogitBoost, BeggRep, BeggJ48 and Random Forest are the ensemble method used in this research. The study shows that LogitBoost is the most accurate model than other ensemble approaches.

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Section: Resultsmentioning

confidence: 99%

A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

Nahar

Ara

Neloy

et al. 2019

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET)

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“…In the future, we will use some deep learning methods for gender classification [27], [28], [29], [30]. and also apply some methodology to remove uncertainty [31], [32], [33], [34], [35], [36]. However, in this study, we have used only 1100 data for the training set.…”

Section: Discussionmentioning

confidence: 99%

Gender Classification from Inertial Sensor-Based Gait Dataset

Pathan

Uddin

Nahar

et al. 2021

Advances in Intelligent Systems and Computing

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The identification of people's gender and events in our everyday applications by means of gait knowledge is becoming important. Security, safety, entertainment, and billing are the examples of such applications. Many technologies could also be used to monitor people's gender and activities. Existing solutions and applications are subject to the privacy and the implementation costs and the accuracy they have achieved. For instance, CCTV or Kinect sensor technology for people is a violation of privacy, since most people don't want to make their photos or videos during their daily work. A new addition to the gait analysis field is the inertial sensor-based gait dataset. Therefore, in this paper, we have classified people's gender from an inertial sensor-based gait dataset. We have collected the gait dataset from Osaka University. Four machine learning algorithms have been applied to identify people's gender and they are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Bagging, and Boosting algorithm. For classifying gender, some useful features are extracted from the raw data, and 84 features have been used to identify people's gender. After feature selection the experimental outcome exhibits the accuracy of gender identification via the Bagging stands at around 87.858%, while it is about 86.09% via SVM. This will in turn form the basis to support human wellbeing by using gait knowledge.

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“…Though CNN is an unexplainable technique based on backpropogation, integrating [31] it with BBRES can overcome this limitation. BRBES [7,10,23] and CNN can assist each other in a symbiotic fashion. Therefore, in future we will integrate BRBES with CNN to improve the current results.…”

Section: Discussionmentioning

confidence: 99%

A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis

et al. 2021

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Parkinson's disease is a neurological disorder. It affects the structures of the central and peripheral nervous system that control movement. One of the symptoms of Parkinson's disease is difficulty in speaking. Hence, analysis of speech signal of patients may provide valuable features for diagnosing. Previous works on diagnosis based on speech data have employed machine learning and deep learning techniques. However, these approaches do not address the various uncertainties in data. Belief rule based expert system (BRBES) is an approach that can reason under various forms of data uncertainty. Thus, the main objective of this research is to compare the potential of BRBES on various speech signal features of patients of parkinson's disease. The research took into account various types of standard speech signal features such MFCCs, TQWTs etc. A BRBES was trained on a dataset of 188 patients of parkinson's disease and 64 healthy candidates with 5fold cross validation. It was optimized using an exploitive version of the nature inspired optimization algorithm called BRB-based adaptive differential evolution (BRBaDE). The optimized model performed better than explorative BRBaDE, genetic algorithm and MATLAB's FMIN-CON optimization on most of these features. It was also found that for speech based diagnosis of Parkinson's disease under uncertainty, the features such as Glottis Quotient, Jitter variants, MFCCs, RPDE, DFA and PPE are relatively more suitable.

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A belief rule-based expert system to assess mental disorder under uncertainty

Cited by 15 publications

References 14 publications

A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

Gender Classification from Inertial Sensor-Based Gait Dataset

A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis

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