Search Results for author:
Found 3 papers, 1 papers with code
L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs
Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing.
Prognosis and Treatment Prediction of Type-2 Diabetes Using Deep Neural Network and Machine Learning Classifiers
Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity. The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with high accuracy, in order to identify and treat diabetes patients at an early age. Our training and test dataset is an accumulation of 9483 diabetes patients information. The training dataset is large enough to negate overfitting and provide for highly accurate test performance. We use performance measures such as accuracy and precision to find out the best algorithm deep ANN which outperforms with 95. 14% accuracy among all other tested machine learning classifiers. We hope our high-performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models.
