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Diabetic diagnosis is an important research in health care domain to analyze relevant microorganisms at an earlier stage. Due to large growth in world’s population, feature subset selection model receives a great deal in any domain of research and also a reliable tool for diabetic diagnosis. Several data mining techniques have been developed to evaluate the significant causes of diabetes with least sets of risk factors. These minimum set is selected without considering the potential significance of the risk factors and optimal feature subset selection, hence it failed to diagnosis the pattern of diabetes accurately. In order to improve the feature subset selection, an Integration of Fuzzy Rough Set Theory and Optimized Genetic algorithm (IFRST-OGA) is introduced. The main objective of the IFRST-OGA is to find optimal risk factors for efficient pattern recognition on diabetes healthcare data. Initially, feature selection is performed using Fuzzy Rough Set Theory (FRST) for diagnosing the diabetes. After that, the Optimized Genetic Algorithm (OGA) is applied which mainly searches for an optimal feature subset through the selection, crossover, and mutation operations to diagnose the disease at an earlier stage. This helps to identify the risk factor and diagnosing the diabetes disease efficiently. Experimental results show that the proposed IFRST-OGA increases the performance in terms of true positive rate, computation time and diabetes diagnosing accuracy.

Keywords

Diabetic Diagnosis, Risk Factors Analysis, Rough Set Theory, Feature Selection, Optimized Genetic Algorithm, Selection, Crossover, Mutation, Optimal Feature Subset Selection.

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References

T. Santhanam and M.S Padmavathi, “Application of K-Means and Genetic Algorithms Dimension Reduction by Integrating SVM for Diabetes Diagnosis”, Procedia Computer Science, Vol. 47, pp. 76-83, 2015.

Fei Ye, “Evolving the SVM Model based on a Hybrid Method using Swarm Optimization Techniques in Combination with a Genetic Algorithm for Medical Diagnosis”, Multimedia Tools and Applications, pp. 1-30, 2016.

Kung-Jeng Wang, Angelia Melani Adrian, Kun-Huang Chen , Kung-Min Wang, “An Improved Electromagnetism-like Mechanism Algorithm and its Application to the Prediction of Diabetes Mellitus”, Journal of Biomedical Informatics, Vol. 54, pp. 220-229, 2015.

Madonna M. Roche and Peizhong Peter Wang, “Factors Associated with a Diabetes Diagnosis and Late Diabetes Diagnosis for Males and Female”, Journal of Clinical and Translational Endocrinology, Vol. 1, pp. 77-84, 2014.

Ioannis Kavakiotis, Olga Tsave , Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas and Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal, Vol. 15, pp. 104-116, 2017.

Jiye Liang, Feng Wang, Chuangyin Dang and Yuhua Qian, “A Group Incremental Approach to Feature Selection Applying Rough Set Technique”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 2, pp. 294-308, 2014.

Mohamed Amine Chikh, Meryem Saidi and Nesma Settouti, “Diagnosis of Diabetes Diseases using an Artificial Immune Recognition System with Fuzzy K-Nearest Neighbor”, Journal of Medical Systems, Vol. 36, No. 5, pp. 2721-2729, 2012.

Mustafa Serter Uzer, Nihat Yilmaz and Onur Inan, “Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification”, The Scientific World Journal, Vol. 2013, pp. 1-10, 2013.

Chih-Fong Tsai, William Eberle and Chi-Yuan Chu, “Genetic Algorithms in Feature and Instance Selection”, Knowledge-Based Systems, Vol. 39, pp. 240-247, 2013.

Divya Tomar and Sonali Agarwal, “Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes”, Advances in Artificial Neural Systems, Vol. 2015, pp. 1-10, 2015.

Filippo Amato, Alberto Lopez, Eladia Maria Pena-Mendez, Petr Vanhara, Ales Hamp and Josef Havel, “Artificial Neural Networks in Medical Diagnosis”, Journal of Applied Biomedicine, Vol. 11, pp. 47-58, 2013.

Abid Sarwar and Vinod Sharma, “Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2”, International Journal of Computer Application, Vol. 3, pp. 14-16, 2012.

A. Pradhan, G.R. Bamnote, Vinit Tribhuvan, Kiran Jadhav, Vijay Chabukswar, Vijay Dhobale, “A Genetic Programming Approach for Detection of Diabetes”, International Journal of Computational Engineering Research, Vol. 2, No. 6, pp. 91-94, 2012.

Liying Fang, Han Zhaoa, Pu Wanga, Mingwei Yud, Jianzhuo Yana, Wenshuai Cheng and Peiyu Chen, “Feature Selection Method based on Mutual Information and Class Separability for Dimension Reduction in Multidimensional Time Series for Clinical Data”, Biomedical Signal Processing and Control, Vol. 21, pp. 82-89, 2015.

Ahmed Hamza Osman and Hani Moetque Aljahdali, “Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM”, International Journal of Advanced Computer Science and Applications, Vol. 8, No. 1, pp. 236-244, 2017.

Xue-Hui Meng, Yi-Xiang Huang, Dong-Ping Rao, Qiu Zhang and Qing Liu, “Comparison of Three Data Mining Models for Predicting Diabetes or Prediabetes by Risk Factors”, Kaohsiung Journal of Medical Sciences, Vol. 29, pp. 93-99, 2013.

Razieh Sheikhpour and Mehdi Agha Sarram, “Diagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms”, Iranian Journal of Diabetes and Obesity, Vol. 6, No. 2, pp. 74-84, 2014.

Fatma Patlar Akbulut and Aydın Akan, “Support Vector Machines Combined with Feature Selection for Diabetes Diagnosis”, Istanbul University-Journal of Electrical and Electronics Engineering, Vol. 17, No. 1, pp. 3219-3225, 2017.

Dilip Kumar Choubey and Sanchita Paul, “GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis”, International Journal of Intelligent Systems and Applications, Vol. 1, pp. 49-59, 2016.

Ahmad Taher Azar and Aboul Ella Hassanien “Dimensionality Reduction of Medical Big Data using Neural-Fuzzy Classifier”, Soft Computing, Vol. 19, No. 4, pp. 1115-1127, 2015.

Ahmed Hamza Osman Hari “Diabetes Disease Diagonosis method based on Feature Extraction using KSVM”, International Journal of Advanced Computer Science and Application, Vol. 8, No. 1, pp. 236-244, 2017.

M. Sudha, “Evolutionary and Neural Computing Based Decision Support system for Disease Diagnosis from Clinical Data set in Medical Practice”, Journal of Medical Systems, Vol. 41, No. 11, pp. 178-183, 2017.

Prediction of Crop Growth using Machine Learning Based on Seed Features

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Authors

N. Nandhini ¹, J. Gowri Shankar ¹

Affiliations
1 Department of Computer Science and Engineering, Knowledge Institute of Technology, IN

Source

ICTACT Journal on Soft Computing, Vol 11, No 1 (2020), Pagination: 2232-223

Abstract

The presence of plant species in the wrong place and time is identified as plant weeds. The loss of yield could result from interference with plant crop weed species. To classify the plant weeds among the seeds, seed classification is carried out in this paper. Here the image of seeds and datasets of sample seed are input. In pre-processing stage the seed image is given as input. An unwanted seeds are removed by comparing the features of seed with sample seeds features by using ID3 algorithm. One of the reasons for failure in crop yield production is selecting suitable soil for crop. As sample dataset contains the detail of growth of crop in soil, it will help for selecting the suitable soil for seeds. The features of sample dataset are compared with the features extracted from the affected crop and predict the disease and prevention measures taken place. In this method prediction is done only after the growth of crop which leads to decrease in quality of crop growth. In order to overcome these issues, diseases can be predicted using seed features by comparing the features with sample dataset. Using Support Vector Machine algorithm the seeds are classified based on the growth and predicting the diseases of crop. This is done by training the dataset by comparing the features extracted from new seeds and features of sample seeds and predicting the crop growth and diseases. Based on prediction of crop growth and crop diseases, preventive measures takes place.

Keywords

Data Mining, Big Data, Feature Extraction, Crop Growth

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