Gensini score prediction using Artificial Neural Networks

Introduction:

• Role of Metabolic Obesity and Body Mass Index in patients of various age group with coronary artery diseases.
  
• Metabolic Obesity (Insulin resistance syndrome).
  
• Indian subcontinent is highly predisposed to this condition.
  
• Prevalence of Insulin resistance syndrome among Indians (≥30%).
  
• Among females is higher than males (50%).
  

Problem definition:

Train a model capable of predicting the GENSINI score which determines the severity of CAD in the following groups:

• Metabolically Healthy Normal Weight (MHNW)
  
• Metabolically Obese Normal Weight (MONW)
  
• Metabolically Healthy Obese (MHO)
  
• Metabolically Abnormal Obese (MAO)
  

Gensini Scoring:

• It is a scoring system for determining the severity of coronary heart disease.
  
• It provides an accurate stratification of patients according to the functional significance of their disease.
  
• It provides an opportunity to match patients with similar degrees of coronary artery disease who are receiving different forms of treatment.
  

Objective:

• To find the group showing a good association to severity of Coronary artery disease that is which category is more prone to CAD, metabolically obese or phenotypically obese.
  
• To find the prognostic markers for CAD among factors like HBA1C, FI, HOMA IR, TC, TG, HDL, LDL and hsCRP and which group shows more association.
  
• Several algorithms were compared for predicting the GENSINI scores from the given features. Algorithms used are:
  
  • Ridge Regression
    
  • LASSO Regression
    
  • Neural Networks
    

Scope/Importance of Project:

• There are no study done in India in relation to importance of Metabolic Obesity and BMI status with severity of Coronary artery disease.
  
• Helpful to find how the Insulin resistance, hsCRP and Lp(a) is associated with the severity of Coronary artery disease.
  
• Effect of Lifestyle modification on Body Mass Index and Waist Circumference in post angioplasty patients.