Paper Title
DISEASE PREDICTION ON THE CO-RELATION BETWEEN THE SYMPTOMS SHOWN AND THEIR EFFECTS ON THE DISEASES USING FEED FORWARD MODEL

Abstract
Disease prediction is a crucial component in healthcare that significantly aids in early diagnosis, efficient treatment planning, and better patient outcomes. In this study, we propose a Feedforward Neural Network (FFNN) model to predict disease occurrence based on patient health data. The FFNN is trained on a dataset containing multiple health indicators, including demographic, lifestyle, and clinical features. The model employs supervised learning techniques to classify patients as having a disease or not based on input features. To enhance prediction accuracy, the FFNN architecture is optimized by tuning the number of hidden layers, neurons, and activation functions [1].Our model is evaluated on a standard medical dataset, with metrics such as accuracy, precision, recall, and F1-score used to assess its performance. The results demonstrate that the FFNN model achieves high accuracy in predicting diseases, outperforming traditional statistical methods in some cases. For example, studies have shown that FFNNs consistently achieve higher accuracy compared to logistic regression and decision trees when applied to complex datasets [2]. This research highlights the potential of neural networks in advancing predictive analytics in healthcare, enabling earlier interventions and personalized treatment strategies [ 3]. Keywords - Feed-Forward Neural Networks, Machine Learning.