• Kandregula Jayasri
• Sai Pavitro Mishra
• Gayatri Metta
• Lanka Swanitha
• Dr. Bode Prasad

COVID-19 has created a major impact on healthcare crises across the globe. Many people have affected by this virus with mild to severe symptoms. In most of the cases people with severe symptoms lead to death causing severe problems related to heart , lungs etc. Most of the people lost their lives during the outbreak of covid-19 . Though the recovery rate is good ,most of the people are identified in developing deadly diseases after getting recovered from this virus. On studying the health history records of different types of people affected with covid-19 ,it is derived that in most of the cases people are suffering with diseases related to heart , kidney , lungs etc.The central objective behind this research is to identify the covid-19 patients being affected with such deadly diseases after their recovery so that doctors can easily treat the patients without any complications in future.covid-19 dataset is collected by correcting it with missing and redundant values which is trained with different machined learning algorithms like random forest , decision trees , svm and convolutional neural networks . The proposed model achieves an average accuracy ( 96% ) , precision (95.24%), and recall (92.05%) also prove the utility of the adopted approach in comparison to other techniques for the prediction of diseases.

Covid-19, random forest , convolutional neural networks , Machine learning .

"PREDICTION OF LONG TERM DISEASES CAUSED AFTER COVID-19 USING CNN AND RANDOM FOREST", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.k293-k301, May-2022, Available :http://www.jetir.org/papers/JETIR2205B39.pdf

"PREDICTION OF LONG TERM DISEASES CAUSED AFTER COVID-19 USING CNN AND RANDOM FOREST", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppk293-k301, May-2022, Available at : http://www.jetir.org/papers/JETIR2205B39.pdf