Diabetes prediction

Keywords: Diabetes, Prediction

Background

The primary focus of this study revolves around the utilization of an ANN model to predict the likelihood of diabetes occurrence. Diabetes, a chronic metabolic disorder with a significant global prevalence, necessitates early prediction and diagnosis to prevent complications and enhance patient outcomes. To accomplish this, the study employs a dataset containing various clinical features such as Pregnancy, Glucose level, Blood Pressure, Skin Thickness, Insulin level, BMI, Diabetes Pedigree Function, and Age. The target variable, labeled "Outcome," assumes binary values of 0 and 1, indicating the absence or presence of diabetes, respectively. The employed ANN model comprises four hidden layers that harness the "ReLu" activation function to capture intricate relationships within the data by introducing non-linearity. For the output layer, the model employs the "sigmoid" activation function, which suits binary classification tasks. The optimization process relies on the Adam optimizer, known for its efficacy in converging toward optimal solutions.

Aim & Objectives

Aim
To Predict the Diabetics Using DL Techniques
Objective
Implement an effective Artificial Neural Network (ANN) model to predict the likelihood of diabetes occurrence, utilizing clinical features from a dataset. The aim is to enhance early detection and diagnosis of diabetes, thereby improving patient outcomes and reducing complications through accurate prediction using advanced neural network techniques.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —