NNSVM: A Novel Approach for Early Prediction of Human Lifestyle Related Diseases

Authors

  • Apeksha V. Sakhare Research Scholar, School of Engineering & Technology, GHRU Amravati University, Amravati
  • Prasad Lokulwar Professor, Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur

Keywords:

Feature selection, lifestyle Related diseases (LRDs), noncommunicable diseases, prediction, NNSVM

Abstract

Due to the tremendous advancements in public health over the past few decades, life expectancy has increased by roughly 15-20 years on average. Improvements in public policy, economic growth, medical diagnosis, and treatment protocols have accompanied the industrialization and urbanization of the 21st century. Deviations in lifestyle and eating habits dramatically raise the risk of noncommunicable diseases (NCDs) such as obesity, type 2 diabetes, hypertension, dyslipidemia, hypertension, osteoarthritis, sleep apnea, and various forms of cancer. Nearly 36 million deaths worldwide (or 63%) were caused by NCDs. In addition to microbial infection and an unhealthful diet, sedentary work habits and insufficient physical activity were other newly discovered risk factors that mostly caused metabolic diseases. Environmental factors and human lifestyle choices bring on numerous diseases that account for a significant portion of global mortality, and diagnosing these illnesses can occasionally be difficult. We need a trustworthy, practical, accurate, and robust method to diagnose noncommunicable diseases (NCDs) based on lifestyle context to treat them early and effectively. NCDs include cardiovascular diseases (CVD), stroke, diabetes, and some types of cancer, usually called lifestyle related diseases (LRDs), since lifestyle choices significantly impact these ailments. This paper discusses the model concept, Prediction methods. Here, NNSVM model is proposed and implemented to predict the diseases like Diabetes, Depression and Hypertension. The purpose of this study is to suggest a framework for early prediction of disorders related to lifestyle that consists of three essential modules: a feature selection module, a disease prediction module, and to suggest remedies.

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References

Farooqui ME, Ahmad DJ. A Detailed Review on Disease Prediction Models That Uses Machine Learning. Int J Innov Res Comput Sci Technol. 2020;8(4):326–330. doi:10.21276/ijircst.2020.8.4.14.

Ambekar S, Phalnikar R. Disease Risk Prediction by Using Convolutional Neural Network. Proc. - 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018. 2018;1-5. doi:10.1109/ICCUBEA.2018.8697423.

Farooqui ME, Ahmad DJ. Disease Prediction System using Support Vector Machine and Multilinear Regression. SSRN Electron J. 2020;331–336. doi:10.2139/ssrn.3673232.

Dahiwade GP, Dahiwade EM. Designing Disease Prediction Model Using Machine Learning Approach. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). 2019;1211–1215.

Das H, Naik B, Behera HS. Medical disease analysis using Neuro-Fuzzy with Feature Extraction Model for classification. Informatics Med Unlocked. 2020;18. doi:10.1016/j.imu.2019.100288.

Sakhare AV, Sharma AK. Analysis and modelling of contextual activity pattern for identifying lifestyle disorders. In: 12th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2021. 2021;203–207.

Deepthi Y, Kalyan KP, Vyas M, Radhika K, Babu DK, Krishna Rao NV. Disease prediction based on symptoms using machine learning. Lecture Notes in Electrical Engineering. 2020;664:561–569. doi:10.1007/978-981-15-5089-8_55.

Kanchan BD, Kishor MM. Study of machine learning algorithms for special disease prediction using principal of component analysis. In: Proceedings - International Conference on Global Trends in Signal Processing, Information Computing and Communication, ICGTSPICC 2016. 2017;5–10. doi:10.1109/ICGTSPICC.2016.7955260.

Kohli PS. Application of Machine Learning in Disease Prediction. In: 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020. 2020;1–4.

Reddy ISV, Kumar SM, Chokkalingam SP. Disease prediction using data mining. Test Eng. Manag. 2019;81(11–12):5490–5493.

Jena L, Swain R. Work-in-Progress: Chronic Disease Risk Prediction Using Distributed Machine Learning Classifiers. In: Proceedings - 2017 International Conference on Information Technology, ICIT 2017. 2018;170–173. doi:10.1109/ICIT.2017.46.

Saboji RG, Ramesh PK. A scalable solution for heart disease prediction using classification mining technique. 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput. ICECDS 2017. 2018;1780–1785. doi:10.1109/ICECDS.2017.8389755.

Mohan S, Thirumalai C, Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 2019;7:81542–81554. doi:10.1109/ACCESS.2019.2923707.

Shukla R, Yadav V, Pal PR, Pathak P. Machine learning techniques for detecting and predicting breast cancer. International Journal of Innovative Technology and Exploring Engineering. 2019;8(7):2658–2662.

Chauhan A, Jain A, Sharma P, Deep V. Heart Disease Prediction using Evolutionary Rule Learning. In: Int. Conf. "Computational Intell. Commun. Technol. CICT 2018. 2018;doi:10.1109/CIACT.2018.8480271.

Dewan A, Sharma M. Prediction of heart disease using a hybrid technique in data mining classification. In: 2015 Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2015. 2015;704–706.

Lutimath NM, Chethan C, Pol BS. Prediction of heart disease using machine learning. International Journal of Recent Technology and Engineering. 2019;8(2 Special Issue 10):474–477. doi:10.35940/jute.B1081.0982S1019.

Senthilkumar B, et al. Ensemble Modelling for Early Breast Cancer Prediction from Diet and Lifestyle. IFAC-PapersOnLine. 2022;55(1):429–435. doi:10.1016/J.IFACOL.2022.04.071.

Tabish SA. Lifestyle Diseases : Consequences, Characteristics, C and Control. 2020;2–5.

Naheeda P, Sharifullah K, Ullah SS, Azeem AM, Shahzad Y, Kinza W. Development of a cost-effective CVD prediction model using lifestyle factors. A cohort study in Pakistan. Afri Health Sci. 2020;20(2):849. doi:10.4314/ahs.v20i2.39.

Tang L, Alelyani S. Feature selection for classification: a review. 2014.

Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–1182.

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Published

29.01.2024

How to Cite

Sakhare, A. V. ., & Lokulwar, P. . (2024). NNSVM: A Novel Approach for Early Prediction of Human Lifestyle Related Diseases. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 565 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4622

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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