Thyroid Detection System - 8th
Thyroid Detection System - 8th
Thyroid Detection System - 8th
Data base
Inference
engine
APPLICATION OF FUZZY LOGIC IN DIAGNOSING DISEASE
Supervised
learning
Machine
learning
Unsupervised
learning
SUPERVISED LEARNING
output of the network is
matched the teacher
information. output of the
network is matched the
teacher information.
supervised
learning
Classification Regression
UNSUPERVISED LEARNING
Unsupervised learning
does not require an
instructor
Unsupervised
learning
Clustering Association
CLUSTERING
Clustering is a method of
unsupervised learning, and a
common technique for
statistical data analysis used in
many fields
ALGORITHM USED FOR CLUSTERING
Fuzzy c-means clustering algorithm
Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or
more clusters.
This method is frequently used in pattern recognition. It is based on minimization of the following
objective function:
Where,
m is any real number greater than 1,
uij is the degree of membership of xi in the cluster j,
xi is the ith of d-dimensional measured data,
cj is the d-dimension center of the cluster
and ||*|| is any norm expressing the similarity between any measured data and the center.
ALGORITHM USED FOR CLASSIFICATION
Fuzzy partitioning is carried out through an iterative optimization of the objective function shown
above, with the update of membership uij and the cluster centres cj by:
where is a termination criterion between 0 and 1, whereas k are the iteration steps.
ALGORITHMIC STEPS FOR FUZZY C-MEANS CLUSTERING
1. Initialize U=[uij] matrix, U(0)
NCBI Aggregation
Sampling
STEPS FOLLOWED IN THE SYSTEM
Step-1 : Importing the dataset and preprocessing the data.
Step-2 : Input number of clusters , fuzziness factor and number of iteration.
Step-3 : Randomly create a membership matrix U satisfying the sum of the elements
equal 1
Step-4 : Compute the centroid of clusters.
Step-5 : Calculate the Euclidean distance between each data object and centriod .
Step-6 : Update the membership matrix.
Step-7 : Assign the data object to the membership matrix which is maximum.
Step-8 : Calculate the objective function.
Step-9 : Repeat steps 3 to 8 until change of membership matrix is very small.
Step-10: Train the dataset by using the above steps through 3 to 9.
Step-11: Predict the clusters .
Step-12: Calculate the Accuracy , Precision of the predicted outcome.
CONCLUSION
In this study, a fuzzy rule-based expert system for diagnosing hypothyroidism disorder was developed
Based on this study, future developments of fuzzy control and monitoring technologies in medicine and
healthcare can be forecast
Fuzzy logic provides a means for encapsulating the subjective decision making process in an algorithm
suitable for computer implementation
Furthermore, the principles behind fuzzy logic are straightforward and its implementation in software is
relatively easy
Because of fuzzy system high interpretability, they are close to human's language, easy to interact with
human experts.to use it as a supplementary model for predicting thyroid
In less developed countries due to lack of expertise and equipment, the number of physicians per ordinary
person is very low. In such a situation, the use of an accurate predictive model can be beneficial
References