Multiple Disease Prediction Using Multitask Residual Shrinkage Convolutional Neural Network

Multiple Disease Prediction Using Multitask Residual Shrinkage Convolutional Neural Network

Description:

The primary goal of this study is to create an efficient and accurate disease prediction system based on the Multitask Residual Shrinkage Convolutional Neural Network (MRS-CNN).

            This system leverages advanced machine learning techniques to predict diseases based on clinical data and other input features.Rather than being disease-specific, it is designed to predict a variety of diseases from a wide range of clinical features,making it applicable to several areas in healthcare,such as cardiology, oncology, neurology, etc.

    The MRS-CNN model employs a multitask learning approach, where multiple tasks are predicted simultaneously. For example, it could predict the likelihood of multiple diseases (e.g., cardiovascular diseases, diabetes, cancer) using the same set of input features,allowing the model to learn shared patterns among different diseases. This method is more efficient than training separate models for each disease, as it allows the system to share information across tasks,which leads to better performance and faster learning.

Input     : Symptoms

Output  : Predicted Disease, Disease Discription, Precautions

Tags:

#DiseasePrediction, #MultitaskLearning, #MRS_CNN, #MachineLearning, #AI, #ConvolutionalNeuralNetwork, #DeepLearning, #HealthcareAI, #Disease, #MedicalAI, #HealthTech, #AIinHealthcare, #PredictiveAnalytics, #DiseaseDiagnosis, #Multitask, #ResidualShrinkage, #ArtificialIntelligence, #HealthData, #AIApplications, #MachineLearningProject, #TechInMedicine, #HealthcareInnovation, #User.

References:

 1. Arumugam, K., Naved, M., Shinde, P.P., Leiva-Chauca, O., Huaman-Osorio, A. and Gonzales-Yanac, T., 2023. Multiple disease prediction using Machine learning algorithms. Materials Today: Proceedings, 80, pp.3682-3685.

2. Ajmera, D., Pandey, T.N., Singh, S., Pal, S., Vyas, S. and Nayak, C.K., 2024. Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models. EAI Endorsed Transactions on Internet of Things, 10.