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Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks.

Al Haidan A, Abu-Hammad O, Dar-Odeh N - Comput Math Methods Med (2014)

Bottom Line: The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer.The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model.Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%.

View Article: PubMed Central - PubMed

Affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.

ABSTRACT
Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

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Related in: MedlinePlus

The network was a two-layer network with 12 neurons in the first layer and only one neuron in the second layer.
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fig4: The network was a two-layer network with 12 neurons in the first layer and only one neuron in the second layer.

Mentions: Figure 4 shows the constructed network with its synaptic weights. The network was a two-layer network with 12 neurons in the first layer and only one neuron in the second layer.


Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks.

Al Haidan A, Abu-Hammad O, Dar-Odeh N - Comput Math Methods Med (2014)

The network was a two-layer network with 12 neurons in the first layer and only one neuron in the second layer.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4120478&req=5

fig4: The network was a two-layer network with 12 neurons in the first layer and only one neuron in the second layer.
Mentions: Figure 4 shows the constructed network with its synaptic weights. The network was a two-layer network with 12 neurons in the first layer and only one neuron in the second layer.

Bottom Line: The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer.The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model.Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%.

View Article: PubMed Central - PubMed

Affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.

ABSTRACT
Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

Show MeSH
Related in: MedlinePlus