Abstract
Classification is a supervised learning model where the class labels are accurately identified for future samples. Medical data is an important source for understanding and improving health outcomes and classification algorithms are often used to analyze these data. Learning models give significant experiences into the situational needs of patients. Various hypotheses have been carried out on different datasets yet it is truly challenging to track down which model is suitable. Proposed work compares the performance of classification models like LR, DT, SVM, NB, KNN, and RF on various datasets. SVM classifier yields accuracy of 0.59 for the Diabetic dataset as it considers individual model opinion, while RF classifier surpassed them both with accuracy 0.9974 for the breast cancer Wisconsin dataset since it is an ensemble approach that takes majority opinions. These findings highlight the need for careful consideration of the choice of classification model when analyzing medical data and provide valuable insights for researchers and practitioners working with these data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aksoy S, Koperski K, Tusk C, Marchisio G, Tilton JC (2005) Learning Bayesian classifiers for scene classification with a visual grammar. IEEE Trans Geosci Remote Sens 43(3):581–589
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216
Astani M, Hasheminejad M, Vaghefi M (2022) A diverse ensemble classifier for tomato disease recognition. Comput Electron Agric 198:107054
Barakat N, Bradley AP, Barakat MNH (2010) Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed 14(4):1114–1120
Fayn J (2010) A classification tree approach for cardiac ischemia detection using spa-tiotemporal information from three standard ecg leads. IEEE Trans Biomed Eng 58(1):95–102
Ambrish G, Ganesh B, Ganesh A, Srinivas C, Dhanraj, Mensinkal K (2022) Logistic regression technique for prediction of cardiovascular disease. Glob Trans Proc 3(1):127–130. Int Conf Intell Eng Approach (ICIEA-2022)
Hameed N, Shabut AM, Ghosh MK, Hossain MA (2020) Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Syst Appl 141:112961
Hossain E, Hossain MF, Rahaman MA (2019) A color and texture based approach for the detection and classification of plant leaf disease using knn classifier. In: 2019 international conference on electrical, computer and communication engineering (ECCE). IEEE, pp 1–6
Kumari S, Kumar D, Mittal M (2021) An ensemble approach for clas-sification and prediction of diabetes mellitus using soft voting classifier. Int J Cognitive Comput Eng 2:40–46
Li JP, Ul Haq A, Ud Din S, Khan J, Khan A, Saboor A (2020) Heart disease identification method using machine learning classification in e-healthcare. IEEE Access 8:107562–107582
Li M, Nie X, Reheman Y, Huang P, Zhang S, Yuan Y, Chen C, Yan Z, Chen C, Lv X et al (2020) Computer-aided diagnosis and staging of pancreatic cancer based on ct images. IEEE Access 8:141705–141718
Lindner C, Thiagarajah S, Wilkinson JM, Wallis GA, Cootes TF, arcOGEN Consortium et al (2013) Fully automatic segmentation of the proximal femur using random forest regression voting. IEEE Trans Med Imag 32(8):1462–1472
Liu M, Zhang J, Adeli E, Shen D (2019) Joint classification and regression via deep multi-task multi-channel learning for alzheimer’s disease diagnosis. IEEE Trans Biomed Eng 66(5):1195–1206
Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham J, ADNI (2015) Multimodal neuroimaging feature learning for multi-class diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng 62(4):1132–1140
Lyngdoh AC, Choudhury NA, Moulik S (2021) Diabetes disease prediction using machine learning algorithms. In: 2020 IEEE-EMBS conference on biomedical engineering and sciences (IECBES), pp 517–521
Gunjan VK, Kumar S, Ansari MD, Vijayalata Y (2022) Prediction of agriculture yields using machine learning algorithms. In: Proceedings of the 2nd international conference on recent trends in machine learning, IoT, smart cities and applications: ICMISC 2021. Springer, Singapore, pp 17–26
Tsanas A, Little MA, McSharry PE, Spielman J, Ramig L-R (2012) Novel speech signal processing algorithms for high-accuracy classifica-tion of parkinson’s disease. IEEE Trans Biomed Eng 59(5):1264–1271
Piao Y, Piao M, Ryu KH (2017) Multiclass cancer classification using a feature subset-based ensemble from microrna expression profiles. Comput Biol Med 80:39–44
Sambasivam G, Opiyo GD (2021) A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt Inf J 22(1):27–34
Springer DB, Tarassenko L, Clifford GD (2015) Logistic regression-hsmm-based heart sound segmentation. IEEE Trans Biomed Eng 63(4):822–832
Tao R, Zhang S, Huang X, Tao M, Ma J, Ma S, Zhang C, Zhang T, Tang F, Jianping L, Shen C, Xie X (2019) Magnetocardiography-based ischemic heart disease detection and localization using machine learning methods. IEEE Trans Biomed Eng 66(6):1658–1667
Kumar S, Gunjan VK, Ansari MD, Pathak R (2022) Credit card fraud detection using support vector machine. In: Proceedings of the 2nd international conference on recent trends in machine learning, IoT, smart cities and applications: ICMISC 2021. Springer, Singapore, pp 27–37
Gaddam DKR, Ansari MD, Vuppala S, Gunjan VK, Sati MM (2022) A performance comparison of optimization algorithms on a generated dataset. In: ICDSMLA 2020: proceedings of the 2nd international conference on data science, machine learning and applications. Springer, Singapore, pp 1407–1415
Narayana GS, Ansari MD, Gunjan VK (2022) Instantaneous approach for evaluating the initial centers in the agricultural databases using K-means clustering algorithm. J Mob Multimedia 43–60
Kumar S, Ansari MD, Gunjan VK, Solanki VK (2020) On classification of BMD images using machine learning (ANN) algorithm. In: ICDSMLA 2019: proceedings of the 1st international conference on data science, machine learning and applications. Springer, Singapore, pp 1590–1599
Gunjan VK, Prasad PS, Pathak R, Kumar A (2020) Machine learning methods for extraction and classification for biometric authentication. In: ICDSMLA 2019: proceedings of the 1st international conference on data science, machine learning and applications. Springer, Singapore, pp 1984–1988
Kumar MR, Gunjan VK (2020) Review of machine learning models for credit scoring analysis. IngenierÃa Solidaria 16(1)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saketha Rama, B.V., Suryanarayana, G., Ansari, M.D., Begum, R. (2023). An Empirical Comparison of Classification Machine Learning Models Using Medical Datasets. In: Kumar, A., Gunjan, V.K., Hu, YC., Senatore, S. (eds) Proceedings of the 4th International Conference on Data Science, Machine Learning and Applications. ICDSMLA 2022. Lecture Notes in Electrical Engineering, vol 1038. Springer, Singapore. https://doi.org/10.1007/978-981-99-2058-7_29
Download citation
DOI: https://doi.org/10.1007/978-981-99-2058-7_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2057-0
Online ISBN: 978-981-99-2058-7
eBook Packages: Computer ScienceComputer Science (R0)