Role of Feature Selection in Building High Performance Heart Disease Prediction Systems

Ekta Maini, Bondu Venkateswarlu, Arbind Gupta

Abstract


In the last few years, there has been a tremendous rise in the number of deaths due to heart diseases all over the world. In low- and middle-income countries, heart diseases are usually not detected in early stages which makes the treatment difficult. Early diagnosis can help significantly in preventing these diseases. Machine learning-based prediction systems offer a cost-effective and efficient way to diagnose these diseases in an early stage. Research is being carried out to increase the performance of these systems. Redundant and irrelevant features in the medical dataset deteriorate the performance of prediction systems. In this paper, an exhaustive study has been done to improve the performance of the prediction systems by applying 4 feature selection algorithms. Experimental results prove that the use of feature selection algorithms provides a substantial increase in accuracy and speed of execution of the prediction system. The prediction system proposed in this study shall prove to be a great help to prevent heart diseases by enabling the medical practitioners to detect heart diseases in early stages.


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References


Dorairaj Prabhakaran, Kavita Singh, Gregory A. Roth, Amitava Banerjee, Neha J. Pagidipati, Mark D. Huffman."Cardiovascular Diseases in India Compared With the United States".Journal of the American College of Cardiology, Vol.72, No.1,2018

India State-Level Disease Burden Initiative Collaborators.Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study. Lancet 2017; 390:2437–60

S. R. Bhagyashree Kiran, Nagaraj, Martin Prince, Caroline H. D., "Murali Krishna, "Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India" Soc Psychiatry Psychiatr Epidemiol

Nimai Chand Das Adhikari, "Prevention of Heart Problem using Artificial Intelligence" International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018

Maini E., Venkateswarlu B., Gupta A., Applying Machine Learning Algorithms to Develop a Universal Cardiovascular Disease Prediction System. In: J. Hemanth et al. (Eds.): ICICI 2018, LNDECT 26, pp. 627–632, 2019. doi:10.1007/978-3-030-03146-6_69

Gupta N, Ahuja N, Malhotra S, Bala A,Kaur G,†Intelligent heart disease prediction in cloud environment through ensembling†Expert Systems.

;34:e12207.https://doi.org/10.1111/exsy.12207

Kavitha, R., Kannan, E., 2016. An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining. International Conference on Emerging Trends in Engineering, Technology, and Science (ICETETS), pp. 1–5

Burak Kolukisa*1, Hilal Hacilar1, Gokhan Goy, Mustafa Kus, Burcu Bakir-Gungor, Atilla Aral, Vehbi Cagri Gungor,"Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease"2018 IEEE International Conference on Big Data (Big Data)

Divya Jain , Vijendra Singh ,"Feature selection and classification systems for chronic disease prediction: A review" Egyptian Informatics Journal 19 (2018) 179–189

Nalband S, Sundar A, Prince AA, Agarwal A. Feature selection and classification methodology for the detection of knee-joint disorders. Comput Methods Programs Biomed 2016;127:94–104.

Mohammad Shafenoor Amina, Yin Kia Chiama, Kasturi Dewi Varathan,"Identification of significant features and data mining techniques in predicting heart disease" Telematics and Informatics 36 (2019) 82–93

Ali Muhammad Usman, Umi Kalsom Yusof, Syibrah Naim "Cuckoo inspired algorithms for feature selection in heart disease prediction" International Journal of Advances in Intelligent Informatics ISSN 2442-6571 ol. 4, No. 2, July 2018, pp. 95-106

Balakrishnan S, Narayanaswamy R, Savarimuthu N, Samikannu R. SVM ranking with the backward search for feature selection in type II diabetes databases. In: Systems, man and cybernetics, 2008. SMC 2008. IEEE international conference on. IEEE; 2008. p. 2628–33.

https://archive.ics.uci.edu/ml/datasets/heart+diseaseG. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,†Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references)


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