Construction and Evaluation of Medical Data Prediction Model Based on Intelligent Algorithms

With the rapid growth of medical data, accurately predicting the health status of patients to support personalized medical decisions has become one of the key issues in the medical field. Therefore, this article introduces Long Short Term Memory (LSTM) networks to construct an efficient model for medical data prediction. Firstly, this study performs data cleaning, missing value filling, and standardization on the collected medical data to ensure data quality and reduce noise interference. Next, through feature selection and construction, key health indicators are used as input features to ensure that the model can effectively capture feature information related to the target variable. Subsequently, based on the characteristics of the LSTM model and its ability to capture long-term dependencies in time series, predictive training is conducted on medical data. Finally, the stability of the model is evaluated using cross validation and compared with other algorithmic models. In the above experimental conclusions,the referenced LSTM model significantly outperforms ARIMA (Autoregressive Integrated Moving Average) and GRU (Gated Recurrent Unit) methods in terms of prediction accuracy and robustness, with a Mean Squared Error (MSE) of 0.0056 and a Mean Absolute Error (MAE) of 0.0601, verifying its effectiveness and superiority in predicting complex medical time series data.