SA: SIMULATED ANNEALING FOR ANALYZING COVID-19 FEMALE RECOVERY PATIENTS MENTAL HEALTH USING DEMOGRAPHIC FACTORS

Dr. Subhani Shaik, Dr. K. Vijayalakshmi, K Gowri Priya, V. Vismitha, J. Saiteja

Abstract


The COVID-19 epidemic has been causing chaos on the society, rendering it in ruins. WHO predicts that in March 2020, the world has taken a significant toll on people, leading to a lot of distortion in their lifestyle? Mental health has gone for a toss and overlooked. People in India do not have the privilege to expect support and deal with their mental health. It is high time to bridge this gap, and we have attempted to do using unconventional approaches like the booming technologies. The proposed model is validated against other baseline techniques like naive-bayes, gradient-boosting, xgboost, catboost, lightgbm and optimization techniques like simulated annealing using svm. The proposed method outperforms other baseline techniques for attaining better accuracy. They are particularly suited to predicting psychological problems. For implementation purposes, choose features like age, family history, seek_help, employment, and a few other features. The proposed model is evaluated with a COVID-19 dataset based on various performance matrices to show its effectiveness.

Keywords


pandemic, classification, model, age, psychological, health, consequences, optimization, techniques, conventional approaches.

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References


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DOI: https://doi.org/10.26483/ijarcs.v13i3.6838

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