Abstract
Advances in computing power and the development of artificial intelligence enable a computer to find hidden insights from data without being programmed. These innovations made revolutions in all parts of science and also in the healthcare field. Machine learning provides a way to automatically find patterns from the data. Medical diagnosis is one of the important aspects of the healthcare domain. The proposed system is intended to make an automated system for disease diagnosis that can infer possible disease from the data given by the user. It can also provide relevant information related to health-related problems. The system can provide an immediate response to the user. Deep Learning, Support Vector Machine (SVM), and Decision Tree are the different machine learning algorithms used in this work to build a computer-aided diagnosis system. Minimum Redundancy Maximum Relevance feature selection method extracting relevant features from disease datasets for training the models. By integrating the outcomes from each classifier using the Ensemble method increases the performance of the system. Using majority voting as the strategy, the ensemble method provides more reliable predictions than the individual classifiers. An efficient automated disease diagnosis system can provide valuable assistance in the healthcare field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fox, S., Duggan, M.: Health Online 2013. Pew Research Center, Survey (2013)
Nie, L., Gang, M., Zhang, L., Yan, S.: Disease inference from health–related questions via sparse deep learning. IEEE Trans. Knowl. Data Eng. 27(8), 2107–2119 (2015)
Shouman, M., Turner, T., Stocker, R.: Using decision tree for diagnosing heart disease patients. In: Proceedings of the 9th Australasian Data Mining Conference, pp. 23–30 (2011)
Ghumbre, S., Patil, C., Ghatol, A.: Heart disease diagnosis using support vector machine. In: Proceedings of the International Conference on Computer Science and Information Technology, pp. 84–88 (2011)
Nie, L., et al.: A joint local-global approach for medical terminology assignment. In: Proceedings of the International ACM SIGIR Workshop, pp. 24–27 (2014)
Nie, L., et al.: Bridging the vocabulary gap between health seekers and healthcare knowledge. IEEE Trans. Knowl. Data Eng. 27(2), 396–409 (2014)
Severyn, A., Moschitti, A.: Learning to rank short text pairs with convolutional deep neural networks. In: Proceedings of the International ACM SIGIR 2015, 09–13 August 2015 (2015)
Hoque, M.M., Quaresma, P.: SEMONTOQA - a semantic understanding-based ontological framework for factoid question answering. In: Proceedings of the International ACM FIRE 2014, 05–07 December 2014 (2014)
Charalampous, K., Kostavelis, I., Amanatiadis, A., Gasteratos, A.: Sparse deep-learning algorithm for recognition and categorization. Electron. Lett. 48(20), 1265–1266 (2012)
Doan, S., Xu, H.: Recognizing medication related entities in hospital discharge summaries using support vector machine. In: Proceedings of the International Conference on Computational Linguistics, pp. 259–266 (2012)
Zhou, J., Yuan, L., Liu, J., Ye, J.: A multi-task learning formulation for predicting disease progression. In: Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery Data Mining, pp. 814–822 (2011)
Khosla, A., et al.: An integrated machine learning approach to stroke prediction. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery Data Mining, pp. 183–192 (2010)
Limsopatham, C., Macdonald, C., Ounis, I.: Learning to combine representations for medical records search. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval pp. 833–836 (2013)
Nie, L., Li, T., Akbari, M., Shen, J., Chua, T.S.: Wenzher: comprehensive vertical search for healthcare domain. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1245–1246 (2014)
Vadicherla, D., Sonawane, S.: Classification of heart disease using SVM and ANN. Int. J. Res. Comput. Commun. Technol. 2(9) (2013)
Frunza, O., Inkpen, D., Tran, T.: A machine learning approach for identifying disease-treatment relations in short texts. IEEE Trans. Knowl. Data Eng. 23(6), 801–814 (2011)
Raja, A.A., et al.: Intelligent syncope disease prediction framework using DM-ensemble techniques. In: Future Technologies Conference (FTC) IEEE, 6–7 December, San Francisco, USA (2016)
Radovic, M., Ghalwash, M., Filipovic, N., Obradovic, Z.: Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform. 18, 9 (2017)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(2), 1–127 (2009)
Bhavsar, H., Panchal, M.H.: A review on support vector machine for data classification. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 1(10), 1–5 (2012)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sunena Rose, M.V., Sobhana, N.V. (2022). Automated Diagnosis of Diseases Using Integrated Machine Learning Approaches. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-96302-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96301-9
Online ISBN: 978-3-030-96302-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)