Novel Machine Learning Algorithms for Predicting COVID-19 Clinical Outcomes with Gender Analysis

The COVID-19 pandemic has created a huge challenge for healthcare services around the world. Understanding the factors affecting treatment outcomes in COVID-19 is important to provide personalized and effective treatment, especially taking into account gender differences. This challenge involves using machine learning to analyze patient data, identify risk factors, and develop predictive models to predict the incidence and severity of COVID-19, including the impact of gender on the disease. This will allow doctors to create treatment plans and allocate resources efficiently based on a person’s gender and other health-related factors. The aim of this article is to develop and evaluate novel machine learning algorithms to predict the clinical outcome of COVID-19 in patients, including the effect of father’s gender. The goal is to develop accurate predictive models that will help doctors predict the progression and severity of COVID-19 in humans, including gender-specific factors in our study I used 7 different ML model with different k fold cross validation and compare with other model to our proposed model and got 99.45 accuracy and other model like random forest accuracy is 94.34, SVM has 95.88 accuracy and other model has got accuracy bellow 95 so we conclude that our proposed model has got best accuracy.