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Approved Research

Prediction of chronic diseases in IT professionals due to Work Life imbalance

Principal Investigator: Mr Karthik Thayyil
Approved Research ID: 101391
Approval date: March 22nd 2023

Lay summary

This research project aims to use machine learning and deep learning algorithms to predict the onset of chronic diseases, such as cardiovascular disease, diabetes, and migraine, in Information Technology (IT) professionals. The study will compare the accuracy of six different predictive models - Random Forest, Naive Bayes, Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine Linear - and use explainable AI techniques to understand which factors within the data contribute the most to accurate predictions.

This research is significant because it can improve the occupational conditions and health of IT workers, leading to a more productive workforce. The study will last for 1 year, with potential for further research over another year. The results of this project will provide valuable insights into the health risks faced by IT professionals and the performance of different predictive models.

Before building the predictive models, we will perform exploratory data analysis, feature selection and feature extraction to pre-process the data and select the most relevant features. This will increase the models' performance and make the predictions more interpretable. We will also analyze the accuracy, precision, and recall of each model and perform statistical analysis of the data.

By using explainable AI, the study will help to understand and interpret the predictions, which can have real-world applications in improving public health. The results of this comparative analysis will provide valuable information about the performance of these six predictive models and enhance the understanding and interpretation of the results.

In conclusion, this study has the potential to provide a better understanding of the health risks faced by IT professionals and the factors that contribute to chronic diseases. The results can help organizations to improve the occupational conditions and overall health and well-being of their IT workforce, leading to a more productive and healthier population.