• Prodduturi Murali Krishna
• K. Saketh
• Md Abdul Rahman
• Ch Harsha Vardhan
• K Keerthana

We developed a new system to predict thyroid disease using two powerful machine learning techniques: Random Forest and LSTM. Our system achieved impressive accuracy, correctly identifying over 93% of cases in one study and 97% in another. This system combines the strengths of both methods, making it effective at both pinpointing key factors and understanding how the disease progresses over time. This promising approach could help doctors diagnose thyroid disease earlier and improve patient care.

" Thyroid disease prediction using machine learning.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i539-i541, April-2024, Available :http://www.jetir.org/papers/JETIR2404871.pdf

" Thyroid disease prediction using machine learning.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi539-i541, April-2024, Available at : http://www.jetir.org/papers/JETIR2404871.pdf