IJETMS LANDING PAGE

International Journal of Engineering Technology and Management Sciences

2023, Volume 7 Issue 2

Prediction of Blood Pressure and Cholesterol By Machine Learning Technique

AUTHOR(S)

Dip Das,Arnam Ghosh, Aishik Banerjee, Mrs. Sulekha Das,Dr. Avijit Kumar Chaudhuri

DOI: https://doi.org/10.46647/ijetms.2023.v07i02.007

ABSTRACT
Blood pressure (BP) is the pressure of circulating blood against the walls of blood vessels. Cholesterol is any of a class of certain organic particles called lipids [1]. High blood pressure along with high cholesterol are two of the main causes of heart disease. Heart disease, stroke, and other cardiovascular (blood vessel) diseases are among the primary cause of death. Approximately 10 million people every year die having high blood pressure and it was found that 6 in 10 Indians have abnormal levels of blood cholesterol [2]. An increase in blood cholesterol signifies an increase in a man's blood pressure, which can easily lead to abnormal blood pressure,e.g. high blood pressure. Generally, Prediction refers to the output of an algorithm after it has been trained on a dataset. Here Authors tried to predict whether a sample can have blood pressure and cholesterol or both present or not by analyzing some datasets. Generally, for making these types of predictions, machine learning algorithms are a very appropriate and most-used technique. A Multiple Linear Regression was applied in this study. Data has been analyzed through Multiple Linear Regression Analysis (MRA). The proposed model is tested on a Primary Data Set, prepared by Techno Engineering College Banipur’s students, and the relevant data of this Dataset has been collected from some districts in West Bengal, India. Through this research, approximately 76 percent of the data was predicted correctly.

Page No: 47 - 55

References:

  1. dozee-early-warning-score-dewsDozee | Hospital Care | Early Warning System, M. Knowledge Centre. Mar 10, 2021.
  2. A. Sreeniwas Kumara,∗ and Nakul Sinhab(2020) Cardiovascular disease in India: A 360-degree overview,49-53
  3. Ghani, I. M. M., & Ahmad, S. (2010). Stepwise multiple regression method to forecast fish landing. Procedia-Social and Behavioral Sciences, 8, 549-554.Breiman,
  4. L. (2001). Random forests. Machine learning, 45(1), 5-32.
  5. Ben-Shakhar, G., Lieblich, I., & Bar-Hillel, M. (1982). An evaluation of polygraphers' judgments: A review from a decision-theoretic perspective. Journal of Applied Psychology, 67(6),701.
  6. Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
  7. Varshaa a; Vinitha V; Usha Nandhini D; Yogeshwaran R; Soundharya B M. "Artificial intelligence and its applications- A Review". International Research Journal on Advanced Science Hub, 1, 2, 2020, 1-4. doi: 10.47392/irjash.2019.11
  8. Darmon, M., Vincent, F., Dellamonica, J., Schortgen, F., Gonzalez, F., Das, V., ... & Schlemmer, B. (2011). Diagnostic performance offractional.
  9. .excretion of urea in the evaluation of critically ill patients with acute kidney injury: a multicenter cohort study. Critical care, 15(4),1-8.
  10. Hajian-Tilaki, K. (2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal.
  11.  Samanta, Animesh, Akash Chowdhury, Dip Das, Arup Kumar Dey, and Mrs.
  12. Sulekha Das. "Prediction through machine learning on the dependence of job prospects in the Afro-American community on proficiency in English."
  13. Saha, Soumayadip, Joyitree Mondal, Arnam Ghosh, Sulekha Das, and Avijit
  14. Kumar Chaudhuri. "Prediction on the Combine Effect of Population, Education, and Unemployment on Criminal Activity Using Machine Learning."
  15. Pal, Saikat Sundar, Soumyadeep Paul, Rajdeep Dey, Sulekha Das, and Avijit Kumar Chaudhuri. "Determining the probability of poverty levels of the Indigenous Americans and Black Americans in the US using Multiple Regression."
  16. Ray, A., & Chaudhuri, A. K. (2021). Smart healthcare disease diagnosis and patient management: Innovation, improvement and skill development. Machine Learning with Applications, 3, 100011.
  17. Chaudhuri, Avijit Kumar, et al. "A multi-stage approach combining feature selection with machine learning techniques for higher prediction reliability and accuracy in cervical cancer diagnosis." Int J Intell Syst Appl 13.5 (2021): 46-63.
  18. Naveenkumar S; Kirubhakaran R; Jeeva G; Shobana M; Sangeetha K. "Smart Health Prediction Using Machine Learning". International Research Journal on Advanced Science Hub, 3, Special Issue ICARD-2021 3S, 2021, 124-128. doi: 10.47392/irjash.2021.079
  19. Chandrakala V; Surya Kumar M S R. "Intelligence slicing: A synthesized framework to integrate artificial intelligence into 5G networks". International Research Journal on Advanced Science Hub, 2, 8, 2020, 57-61. doi: 10.47392/irjash.2020.94
  20.  

    How to Cite This Article:
    Dip Das,Arnam Ghosh, Aishik Banerjee, Mrs. Sulekha Das,Dr. Avijit Kumar Chaudhuri . Prediction of Blood Pressure and Cholesterol By Machine Learning Technique . ijetms;7(2):47-55. DOI: 10.46647/ijetms.2023.v07i02.007