Machine learning plays a crucial role in early disease detection by identifying patterns hidden in medical data. One of the simplest yet most powerful concepts behind many predictive models is the perceptron. Through the implementation of a perceptron from scratch and apply it to a real world healthcare problem which is diabetes prediction.
Problem Statement
Diabetes is a chronic disease that can be managed effectively if detected early. Medical datasets often contain attributes such as glucose level blood pressure BMI age and insulin values. The objective of this project is to classify whether a patient is diabetic or not based on these input features using a perceptron based binary classifier.
Understanding the Perceptron Model
A perceptron acts as a single artificial neuron. It combines multiple input features with learned weights adds a bias and passes the result through an activation function to generate a prediction.
The perceptron outputs a probability indicating the likelihood of diabetes presence.
Core Components Used
Input Features
The dataset contains numerical medical attributes that describe patient health conditions. Each row represents one patient and each column represents a feature.
Weights and Bias
Weights are initialized randomly and adjusted during training. The bias helps shift the decision boundary and improves prediction flexibility.
Activation Function
The sigmoid activation function is used to squash the output into a value between 0 and 1. This makes it suitable for binary classification problems like diabetes prediction.
Building the Model from Scratch
Instead of using ready made models the perceptron is implemented step by step. This includes
Manually initializing weights and bias
Computing the weighted sum of inputs
Applying the sigmoid function
Generating predictions based on a threshold
This approach helps understand the complete flow of data through the model.
Training Process
The model is trained using gradient descent. For each training example the perceptron
Calculates the predicted output
Computes the error between predicted and actual values
Updates weights and bias to reduce the error
This process is repeated for multiple epochs allowing the model to gradually improve its predictions.
Model Evaluation
After training the perceptron is tested on unseen data to evaluate its performance. Accuracy is calculated to measure how well the model predicts diabetes outcomes. Although simple the model demonstrates how learning occurs even with basic neural structures.
Limitations
A single perceptron can only learn linear decision boundaries. Medical data often contains complex relationships which may require multilayer neural networks for higher accuracy.