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Intelligent Heart Disease Prediction on Physical and Mental Parameters: A ML Based IoT and Big Data Application and Analysis

  • Rohit RastogiEmail author
  • D. K. Chaturvedi
  • Santosh Satya
  • Navneet Arora
Chapter
  • 20 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 13)

Abstract

Nearly 17.5 million deaths from cardiovascular disease occur worldwide. Currently, India has more than 30 million heart patients. People’s unconscious attitudes towards health are likely to lead to a variety of illnesses and can be life threatening. In the healthcare industry, large amounts of data are frequently generated. However, it is often not used effectively. The data indicates that the generated image, sound, text, or file has some hidden patterns and their relationships. Tools used to extract knowledge from these databases for clinical diagnosis of disease or other purposes are less common. Of course, if you can create a mechanism or system that can communicate your mind to people and alert you based on your medical history, it will help. Current experimental studies use machine learning (ML) algorithms to predict risk factors for a person’s heart disease, depending on several characteristics of the medical history. Use input features such as gender, cholesterol, blood pressure, TTH, and stress to predict the patient’s risk of heart disease. Data mining (DM) techniques such as Naive Bayes, decision trees, support vector machines, and logistic regression are analyzed in the heart disease database. The accuracy of various algorithms is measured and the algorithms were compared. The result of this experimental analysis is a 0 or 1 result that poses no danger or danger to the individual. Django is used to run a website.

Keywords

Stress Internet of Things (IoT) Mental health Meditation Tension type headache (TTH) Naïve Bayes (NB) Support vector machine (SVM) Machine learning (ML) Logistic regression Mental and physical scores Decision tree (DT) Connected devices/smart devices Big data (BD) tools Big data analysis (BDA) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rohit Rastogi
    • 1
    Email author
  • D. K. Chaturvedi
    • 2
  • Santosh Satya
    • 3
  • Navneet Arora
    • 4
  1. 1.ABES Engineering CollegeGhaziabadIndia
  2. 2.Dayalbagh Educational InstituteAgraIndia
  3. 3.Indian Institute of TechnologyDelhiIndia
  4. 4.Indian Institute of TechnologyRoorkeeIndia

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