scholarly journals Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jamal Salahaldeen Majeed Alneamy ◽  
Rahma Abdulwahid Hameed Alnaish
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Wavelet Neural Network ◽  
Superior Performance ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Fuzzy Wavelet Neural Network ◽  
Heart Disease Diagnosis ◽  
Teaching Learning

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

scholarly journals Neural Network Model for Heart Disease Diagnosis

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Disease Diagnosis ◽  
Batch Size ◽  
Diagnosis System ◽  
Simulation Results ◽  
Heart Disease Diagnosis

The heart disease diagnosis system is proposed inthis study. This kind of diagnosis systems enhance medical careand helps doctors. In this paper, heart disease dataset fromkaggle web site is used. Neural Network is examined andanalyzed for different structures as an optimizer, loss function,and batch size. The simulation results show that the proposedneural network model has 90,16% accuracy.

scholarly journals Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3546
Author(s):  
Nehal Elshaboury ◽  
Eslam Mohammed Abdelkader ◽  
Abobakr Al-Sakkaf ◽  
Ghasan Alfalah
Keyword(s):  
Neural Network ◽  
Wall Thickness ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Distribution Networks ◽  
Elman Neural Network ◽  
Water Pipes ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

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