> (Diagnostic Disease Using Gentic Algorithm ) <
Abstract— A genetic algorithm (Machine Learning approach)
is one of the most important heuristic search techniques from
Evolutionary programming languages. This paper is not
anticipated to provide an inclusive overview but rather
describe some subareas of medical diagnoses techniques like
image processing, Its actually a helpful phenomena which
provides a complete model of symptoms that a person can
identify the disease terminology. The present process uses a
wrapper approach using GA-based on feature selection and
PS-classifier. The results of experiment show that the
proposed model is comparable to the other models. In this
paper, there are many types of cancer functionality is given.
The results show that feature selection can improve accuracy,
specificity and sensitivity of classifiers
Index Terms—classifiers,Np Hard
I. INTRODUCTION
This is a major class of problems in medical science
involves the diagnosis of disease, based on a number of tests
done on the patients. Because of welter of data, the ultimate
diagnosis may be difficult to obtain, even for a medical expert.
One of the application areas of analyzing database is
automated diagnostic systems. This work reviews many basic
algorithms found within the literature and assesses their
performance in a very controlled state of scenario. To solve
this kind of problems genetic algorithm approach uses for
feature selection and parameters optimization. These systems
can help doctors in their decision making. Another application
is finding ways to improve patient outcome, reduce cost and
enhance clinical studies.
GA chose subsets of features for the input of the classifier
(neural network) and the accuracy of the classifier determined
the percentage of effectiveness of each subset of features.
A. Literature Survey
II. GENETIC ALGORITHM BASED FLEXIBLE JOB
SHOP SCHEDULING PROBLEMS
In this paper, Flexible Job Shop Scheduling Problem
(FJSSP) is an extension of the classical Job Shop
Scheduling Problem (JSSP). The FJSSP is known to
be NP-hard problem with regard to optimization
and it is very difficult to find reasonably accurate
solutions of the problem instances in a rational
time. Extensive research has been carried out in
this area especially over the span of the last 20
years in which the hybrid approaches involving
Genetic Algorithm (GA) have gained the most
popularity. Keeping in view this aspect, this
article presents a comprehensive literature
review of the FJSSPs solved using the GA. The
survey is further extended by the inclusion of the
hybrid GA (hGA) techniques used in the solution
of the problem. This review will give readers an
insight into use of certain parameters in their
future research along with future research
directions.
III. AN EXPERT DIAGNOSIS SYSTEM FOR
PARKINSON DISEASE BASED ON GENETIC
ALGORITHM-WAVELET KERNEL-EXTREME LEARNING
MACHINE
Parkinson disease is a major public health
problem all around the world. This paper
proposes an expert disease diagnosis system for
Parkinson disease based on genetic algorithm-
(GA-) wavelet kernel- (WK-) Extreme Learning
Machines (ELM). The classifier used in this paper
is single layer neural network (SLNN) and it is
trained by the ELM learning method. The
Parkinson disease datasets are obtained from the
UCI machine learning database. In wavelet
kernel-Extreme Learning Machine (WK-ELM)
structure, there are three adjustable parameters
Disease Diagnostics Machines using Genetic
Algorithm
Ammara Khan(60081), Syed Junaid Ahmed(61852) , Ahsan Shamim(62107)
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