Human Disease Prediction using Artificial inteligence

Abstract

Disease prediction is an advanced methodology aimed at estimating the likelihood of a patient having a particular disease or illness by evaluating their symptoms. This approach supports timely diagnosis and effective treat, K- Nearest Neighbors (KNN), and RUSBoost, have been increasingly applied to symptom-based disease detection. However, many existing models lack effective preprocessing or transformation of input data, leading to suboptimal accuracy levels. To address this limitation, , enabling patients to receive prompt medical intervention. This model leverages a medical dataset sourced from Kaggle and incorporates preprocessing techniques where symptoms are weighted according to their rarity. A hybrid approach is utilized, combining the Random Forest, Long Short- Term Memory (LSTM) networks, and SVM algorithms for dataset analysis. By integrating a patient’s history and applying LSTM for in-depth disease pattern recognition, the model ensures more precise detection. The final stage of decision- making is handled by SVM to classify diseases effectively. a significant advancement in healthcare automation. By optimizing diagnostic procedures .al field..