Diabetes Mellitus Prediction Using Deep Learning

Abstract

Diabetes mellitus, a habitual metabolic complaint, remains one of the leading global health challenges, with millions of individualities affected worldwide. Early opinion and effective vaticination are vital to managing the condition and reducing the threat of complications. This study focuses on using advanced deep literacy ways to develop a robust frame for diabetes mellitus vaticination. Specifically, Long Short- Term Memory( LSTM) networks and Convolutional Neural Network/ Deep Neural Network( CNN/ DNN) infrastructures are employed to harness their separate strengths in temporal data analysis and point birth. LSTM networks are particularly suited for recycling successional data, similar as patient health records, as they capture temporal dependences within the data. This makes them an ideal choice for assaying time- series health criteria like blood glucose situations, insulin response patterns, and other biomarkers. Meanwhile, CNN/ DNN models are largely effective in rooting intricate patterns from high- dimensional data, enabling them to identify complex connections among the features that contribute to the onset of diabetes. By integrating these two infrastructures, the proposed frame delivers a comprehensive approach to assaying health data and prognosticating diabetes threat.
The experimental results demonstrate the proposed frame’s efficacity in prognosticating diabetes mellitus. It achieved an delicacy of 94.2, a perfection of 92.8, a recall of 93.5, and an AUC- ROC score of 0.96, outperforming traditional machine literacy models. likewise, the frame effectively handles challenges similar as imbalanced datasets and noisy features through data addition and early stopping during training. These results punctuate the advantages of combining LSTM’s successional modeling capabilities with CNN/ DNN’s pattern recognition strengths, offering a largely dependable vaticination system. This study significantly contributes to the field of prophetic healthcare analytics by presenting a scalable and effective result for diabetes vaticination. The integration of LSTM and CNN/ DNN demonstrates the eventuality of deep literacy ways in addressing real- world healthcare challenges. Beyond the emotional performance criteria , the model’s design ensures rigidity, making it applicable to different healthcare surroundings.