Advancing Age as a Risk Factor for Acute Myocardial Infarction

Background: Acute myocardial infarction (AMI) is one of the leading causes of death in developed and developing countries. Age is an important non-modifiable risk factor for acute myocardial infarction. Objectives: The objective of the study was to explore the relationship of advancing age with the risk of acute myocardial infarction. Methods: It was a cross-sectional study conducted in 2019 after getting approval from Institutional Review board of University of Health Sciences, Lahore. Written informed consent and thorough history was taken from the study participants. Group 1 included 45 AMI patients aged 20-60 years. Group 2 included 45 healthy individuals aged 20-60 years. Independent sample t test and chi-square tests were applied for analysis of data. Results: Mean age was significantly higher in AMI patients (50.52±7.31) as compared to healthy controls (30.67±7.20). The risk of AMI increases with advancing age (p<0.001, OR= 2.78). Conclusions: Advancing age is an important risk factor for acute myocardial infarction.

Advancements in Genetic Programming for Data Classification

This thesis aims to advance the state of the art in data classification using Genetic programming (GP). GP is an evolutionary algorithm that has several outstanding features making it ideal for complex problems like data classification. However, it suffers from a few limitations that reduce its significance. This thesis targets at proposing optimal solutions to these GP limitations. The problems covered in this thesis are: 1. Increase in GP tree complexity during evolution that results in long training time. 2. Lack of convergence to a single (optimal) solution. 3. Lack of methodology to handle mixed data-type without type transformation. 4. Search of a better method for multi-class classification. Through this work, we have proposed a method which achieves significant reduction in bloat for classification task. Moreover, we have presented a Particle Swarm Optimization based hybrid approach to increase performance of GP evolved classifiers. The approach offers better performance in less computational effort. Another approach introduces a new two layered paradigm for mixed type data classification with an added feature that uses data in its original form instead of any transformation or pre-processing. The last but not the least contribution is an efficient binary encoding method for multi-class classification problems. The method involves smaller number of GP evolutions, reducing the computation and suffers from fewer conflicts yielding better results. All of the proposed methods have been tested and our experiments conclude the efficiency of proposed approaches.