IRJET- Classification of Chemical Medicine or Drug using K Nearest Neighbor (KNN) and Genetic Algorithm
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This document proposes using a combination of K-nearest neighbors (KNN) and genetic algorithms to classify chemical medicine or drug data with improved accuracy. KNN is described as a simple and effective classification algorithm that stores training data instances. Genetic algorithms are presented as evolutionary algorithms useful for optimization problems. The proposed system applies genetic search to rank attribute importance, selects high-ranked attributes, and then applies both KNN and genetic algorithms to classify the drug data, aiming to improve classification accuracy over using either technique alone. The combination of KNN and genetic algorithms is expected to better optimize classification of complex medical data compared to other algorithms.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 834
2. METHOD:
Here we proposed combines KNN algorithms and genetic
algorithm to improve the classification accuracyofdrugdata
set or medicine. We used to genetic search as better result
measure to prune redundant and irrelevant attributes.
Fig: Proposed System
In this system firstly load the data set, the genetic algorithm
which an evolutionary algorithm is useful for search and
optimization problem. They are apply genetic search
algorithm on the data set are rank based their attributes
values , and then select higher ranked of attributes. We are
applying two algorithm knn and genetic algorithm which
classify better accuracy, the accuracy of the classifier
computed as test data is an no of samplescorrectly classified
divided to total number of sample in test data. Knn and
genetic algorithms we classify chemical drugs or medicine
using training data sample set. The geneticalgorithmutilizes
basic three operations they are selection, mutation and
crossover. These operation have different type of individual
properties such as population size, crossover and mutation
probabilities in a genetic algorithms. In a medical field there
are various application used genetic algorithm such as
oncology, radiology, cardiology, endocrinology, obstetrics
and gynecology, surgery, infectious, diseases, neurologyand
orthopedics.
3. CONCLUSION:
Classification of medical data is highly complex structure,
knn is most effective classification technique to classify
unknown medical data set, better result other algorithm.We
used knn and genetic algorithm to optimize problem. Knn
algorithm categories unknown type of drugs and genetic
algorithm which solve optimal solution, it will improve the
classification accuracy of drug data or medicine data set.
REFERENCES:
[1]. M.Akhil jabbar, B.L Deekshatulu, Priti Chandra,”
Classification of Heart Disease Using K- Nearest
Neighbor and Genetic Algorithm” CIMTA pp.85-94
(2013).
[2]. Dr saed sayad,”University of toronto
http://chem-eng.utoronto.ca/~data mining.
[3]. Nitin Bhatia ,vandana ”Survey on nearest neighbor
techniques” IJCSIS,Vol 80,no 2(2010).
[4]. Max bramer,”Principles of data mining” Springer
(2007).
[5]. S.N Sivanandam, S.N Deepa,”Introduction to genetic
algorithms” Springer (2008).
[6]. S.N. Sivanandam,S.N. Deepa, ”Introduction to
genetic algorithms”Springer (2008) MA.Jabbar,B.L
Deekshatulu, Priti chandra. ”An evolutionary
algorithm forheart disease prediction” CCIS,PP 378-
389, Springer(2012).
[7]. MA. Jabbar, B.L Deekshatulu, Priti chandra,
”Prediction of Risk Score for Heart Disease using
Associative classification and Hybrid Feature Subset
Selection” ,In .Conf ISDA, pp 628-634,IEEE(2013).
[8]. MA. Jabbar, B.L deekshatulu, priti chandra,
”classification of heart disease usingANNandfeature
subset selection” GJCST, VOL 13,issue 3,version1.0
pp15-25(2013).
[9]. D.E Goldberg.” Genetic algorithm in search
optimization and machine learning “ Addisonwesely
(1989)](https://image.slidesharecdn.com/irjet-v5i3188-190110105352/85/IRJET-Classification-of-Chemical-Medicine-or-Drug-using-K-Nearest-Neighbor-KNN-and-Genetic-Algorithm-2-320.jpg)
IRJET- Classification of Chemical Medicine or Drug using K Nearest Neighbor (KNN) and Genetic Algorithm
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 833 Classification of Chemical Medicine or Drug using K nearest Neighbor (kNN) and Genetic Algorithm Pradip A. Sarkate1, Prof. A. V. Deorankar2 1P.G. Student, Department of Computer Science & Engineering, Govt. College of Engineering, Amravati, India 2Associate Professor, Department of Computer Science & Engineering, Govt. College of Engineering, Amravati, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract- Data mining techniques refers to extracting or mine knowledge information from large amount of data. In Data mining classification are supervised learning methods which are used to predict a different design model and describing important a data classes. Data classification in medical field is different from other field. Medical data classification involves multi class classification,heterogeneous and complex data structure. In classification K- Nearest Neighbor are most popular, very simple, highly effective algorithms. Genetic algorithms are most popular techniquein evaluation algorithm that used to solve optimizationproblem. We used k-NN and genetic algorithm to categorize drug data set, which can solve classification and optimization problem which can better optimal result in drug or medicine data set. The k-NN and genetic algorithm techniques which are improved accuracy classification method in drug or medicine data set. Key Words: data mining; KNN algorithm, genetic algorithm. 1. INTRODUCTION: Data mining refers to extracting or“mining"knowledgefrom large amounts of data. Data mining is also called as “knowledge mining in a data”, which integral part of KDD (knowledge data mining) are consist series of transformation step from data preprocessing to the post processing in data mining result. There are various multinational pharmaceutical industries which are developed the medicines which are categories to original research and generic medicines. There are basic functionality of data mining such as clustering association, and classification. Classifications which are used to categories the different types of drug, classifying drug data set. The classification technique is used to improving a drug dataset or an medicine data set the classification is an important role in data mining techniques. The K Nearest Neighbor is a most valuable popular classification algorithms in data mining technique. The genetic algorithm is an evaluation algorithmwhichsolvedan optimization problem. We will define theKNNalgorithmand genetic algorithm 1.1 K Nearest Neighbor algorithm: K nearest neighbor classification algorithm is a instance based learning or non general learning, it will simply stores instance of training data. The nearest neighbor method is to find a predefined number of training samples closest in distance to new point, and predict the label from these instances. The number of samples can be user defined based on local density of point. K nearest neighbor algorithm is calculated on the basis of value of k, which will define how many nearestneighborsare to be considered to define class of a training sample data point. The training sample data points are assigned weights according to their distances from sample data point. Nearest Neighbor techniques are classified into two categories 1. Structure less NN technique and 2. Structure based NN technique. This technique is very simple and easy to implement, The K-nearest neighbor lies in first category whole data is classified into training data and sample data point. Structure based NN techniquesis based on structures of data like orthogonal structure tree (OST), ball tree, k-d tree, axis tree, nearest future line and central line. The nearest neighbor is to find the K training instanceswhichare closest to unknown instance and pick the most commonly occurring classification for these K instances. Application of KNN Classification and interpretation Nearest Neighbor based Content Retrieval Protein-Protein interaction and 3D structure prediction Drawback Low efficiency Dependency on the selection of values for K 1.2 Genetic Algorithm: Genetic algorithm is a most important technique in evolutionary computing which is used to solve an optimization problem. To solve these optimizationproblem evaluation algorithm require a data structure to represent and evaluate solution from old solution’s. A solution generated by genetic algorithm is called by chromosome, while collection of chromosome is called as population.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 834 2. METHOD: Here we proposed combines KNN algorithms and genetic algorithm to improve the classification accuracyofdrugdata set or medicine. We used to genetic search as better result measure to prune redundant and irrelevant attributes. Fig: Proposed System In this system firstly load the data set, the genetic algorithm which an evolutionary algorithm is useful for search and optimization problem. They are apply genetic search algorithm on the data set are rank based their attributes values , and then select higher ranked of attributes. We are applying two algorithm knn and genetic algorithm which classify better accuracy, the accuracy of the classifier computed as test data is an no of samplescorrectly classified divided to total number of sample in test data. Knn and genetic algorithms we classify chemical drugs or medicine using training data sample set. The geneticalgorithmutilizes basic three operations they are selection, mutation and crossover. These operation have different type of individual properties such as population size, crossover and mutation probabilities in a genetic algorithms. In a medical field there are various application used genetic algorithm such as oncology, radiology, cardiology, endocrinology, obstetrics and gynecology, surgery, infectious, diseases, neurologyand orthopedics. 3. CONCLUSION: Classification of medical data is highly complex structure, knn is most effective classification technique to classify unknown medical data set, better result other algorithm.We used knn and genetic algorithm to optimize problem. Knn algorithm categories unknown type of drugs and genetic algorithm which solve optimal solution, it will improve the classification accuracy of drug data or medicine data set. REFERENCES: [1]. M.Akhil jabbar, B.L Deekshatulu, Priti Chandra,” Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm” CIMTA pp.85-94 (2013). [2]. Dr saed sayad,”University of toronto http://chem-eng.utoronto.ca/~data mining. [3]. Nitin Bhatia ,vandana ”Survey on nearest neighbor techniques” IJCSIS,Vol 80,no 2(2010). [4]. Max bramer,”Principles of data mining” Springer (2007). [5]. S.N Sivanandam, S.N Deepa,”Introduction to genetic algorithms” Springer (2008). [6]. S.N. Sivanandam,S.N. Deepa, ”Introduction to genetic algorithms”Springer (2008) MA.Jabbar,B.L Deekshatulu, Priti chandra. ”An evolutionary algorithm forheart disease prediction” CCIS,PP 378- 389, Springer(2012). [7]. MA. Jabbar, B.L Deekshatulu, Priti chandra, ”Prediction of Risk Score for Heart Disease using Associative classification and Hybrid Feature Subset Selection” ,In .Conf ISDA, pp 628-634,IEEE(2013). [8]. MA. Jabbar, B.L deekshatulu, priti chandra, ”classification of heart disease usingANNandfeature subset selection” GJCST, VOL 13,issue 3,version1.0 pp15-25(2013). [9]. D.E Goldberg.” Genetic algorithm in search optimization and machine learning “ Addisonwesely (1989)