Abstract
Machine learning has been used to develop diagnostic tools in the field of medicine for decades. Huge progress has been made in this area, however, a lot more work has yet to be done in order to make it more pertinent for real-time application in our day-to-day life. As a part of data mining, ML learns from previously fed data to classify and cluster relevant information. Hence, the main problems arise due to variations in the big data in the individuals and huge amounts of unorganised datasets. We have used ML to figure out various patterns in our dataset and to calculate the accuracy of this data, with the hope that this serves as a stepping stone towards developing tools that can help in medical diagnosis/treatment in future. Creating an efficient diagnostic tool will help improve healthcare to a great extent. We have used a mixed dataset where an individual with any severe illness in early stages or individuals who are further along, are both present. We use libraries like seaborn to construct a detailed map of the data. The fundamental factors considered in this dataset are age, gender, region of stay and blood groups. The main goal is to compare different data to each other and locate patterns within.
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References
I. Sharma, A. Agarwal, A. Saxena, S. Chandra, Development of a better Study resource for genetic disorders through online platform. Int. J. Inf. Syst. Manag. Sci. 1(2), 252–258 (2018)
S. Mohagaonkara, A. Rawlani, P. Srivastavac, A. Saxena, HerbNet: intelligent knowledge discovery in MySQL database for acute ailments, in 4th International Conference on Computers and Management (ICCM) (ELSEVIER-SSRN, 2018), pp. 161–165. ISSN: 1556-5068
S. Shuklaa, A. Saxena, Python based drug designing for Alzheimer’s disease, in 4th International Conference on Computers and Management (ICCM) (ELSEVIER-SSRN, 2018) pp. 20–24. ISSN: 1556-5068
A Agarwal and A Saxena, Comparing machine learning algorithms to predict diabetes inwomen and visualize factors affecting it the most—a step toward better healthcare forwomen, in International Conference on Innovative Computing and Communications. https://doi.org/10.1007/978-981-15-1286-5_29,2019
A. Saxena, N. Kaushik, A. Chaurasia and N. Kaushik, Predicting the outcome of an election results using sentiment analysis of machine learning, in International Conference on Innovative Computing and Communications. https://doi.org/10.1007/978-981-15-1286-5_43,2019
A. Saxena, S. Chandra, A. Grover, L. Anand and S. Jauhari, Genetic variance study in human on the basis of skin/eye/hair pigmentation using apache spark, in International Conference on Innovative Computing and Communications. https://doi.org/10.1007/978-981-15-1286-5_31,2019
V.V Vijayan, C. Anjali, Prediction and diagnosis of diabetes mellitus-a machine learning approach, in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (Trivandrum, 2015)
B sarvwar, V Sharma, Intelligent Naive Bayes approach to diagnose diabetes type- 2. Int. J. Comput. Appl. Issues Chall. Netw. Intell. Comput. Technol. (2012)
R Motka, V Parmar, Diabetes mellitus forecast using different data mining techniques, in IEEE International Conference on Computer and Communication Technology (ICCCT) 2013
S. Sapna, A. Tamilarasi, M. Pravin, Implementation of genetic algorithm in predicting diabetes. Int. J. Comput. Sci. 9, 234–240
K Savvas, N. Schizas Christos, Region based support vector machine algorithm for medical diagnosis on Pima Indian diabetes dataset, in IEEE Conference on Bioinformatics and Bioengineering (2012), pp. 139–144
A. Al Jarullah, Decision discovery for the diagnosis of Type II Diabetes, in IEEE Conference on Innovations in Information Technology (2011), pp. 303–307
D.M. Nirmala, B.S. Appavu alias, U.V. Swathi, An amalgam KNN to predict Diabetes Mellitus, in IEEE International Conference on Emerging Trends in Computing Communication and Nanotechnology(ICECCN) (2013), pp. 691–695
U Poonam, H Kaur, P Patil, Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination, in International Conference on Pervasive Computing (ICPC) (2015), pp. 1–4
S.S Vinod Chandra, S Anand Hareendran, Artificial Intelligence and Machine Learning (PHI learning Private Limited, Delhi, 2014), p. 110092
R. Bellazzi, B. Zupan, Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Informatics 77, 81–97 (2008)
A. Agarwal, A. Saxena, Malignant tumor detection using machine learning through scikit-learn. Int. J. Pure Appl. Math. 119(15), 2863–2874 (2018)
S. Saria, A.K. Rajani, J. Gould, D. Koller, A.A Penn, Integration of early physiological responses predicts later illness severity in preterm infants. Sci. Trans. Med. 2, 48ra65 (2010)
D.C. Kale, D. Gong, Z. Che et al., An examination of multivariate time series hashing with applications to health car, in IEEE International Conference on Data Mining (ICDM) (2014), pp. 260–69
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We would like to express our deep sense of gratitude towards Amity Institute of Biotechnology and our family, without their support throughout the process this paper would have not been accomplished.
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Mohanty, S., Mishra, A., Saxena, A. (2021). Medical Data Analysis Using Machine Learning with KNN. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_42
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DOI: https://doi.org/10.1007/978-981-15-5148-2_42
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