An artificial neural network-based model for diabetes prediction, leveraging machine learning techniques to analyze relevant health data and provide accurate predictions regarding the likelihood of diabetes.
Artificial Neural Networks (ANNs) represent a class of machine learning models inspired by the intricate architecture and functioning of the human brain. At their core, ANNs consist of interconnected nodes, or artificial neurons, organized in layers—namely, input, hidden, and output layers. This layered structure allows ANNs to process information in a manner akin to biological neural networks, enabling them to learn and recognize complex patterns, relationships, and representations within data.
The learning process in ANNs involves adjusting the weights assigned to connections between neurons based on the network's performance during training. This iterative optimization, often facilitated by algorithms like backpropagation, allows the model to adapt and improve its ability to generalize from the training data to unseen examples. Activation functions within each neuron introduce non-linearities, enhancing the network's capacity to capture intricate relationships and solve complex problems.
Hidden Layers
The network has four hidden layers, each defined as a "Dense" layer. These layers are densely connected, meaning each neuron in a layer is connected to every neuron in the subsequent layer. The number of neurons in these hidden layers progressively increases: 8, 16, 32, and 64. This allows the network to capture increasingly complex patterns and relationships in the data.
In each hidden layer, the Rectified Linear Unit (ReLU) activation function is used. ReLU introduces non-linearity to the model, helping it learn and represent more complex mappings between inputs and outputs.
The output layer consists of a single neuron with a sigmoid activation function. This architecture is typical for binary classification tasks. The sigmoid function squashes the output between 0 and 1, representing the probability of the input belonging to the positive class.
The model is compiled with the 'adamax' optimizer, a variant of the Adam optimizer, which is an algorithm for optimizing the model's weights during training. The loss function used is 'binary_crossentropy,' suitable for binary classification problems, measuring the difference between the predicted probabilities and the actual class labels. The chosen metric for monitoring during training is 'accuracy,' indicating the proportion of correctly classified instances.
The dataset comprises several features related to individuals' health parameters, specifically aimed at predicting the presence or absence of diabetes based on a binary class variable (0 or 1). The features include:
- Number of times pregnant: The count of pregnancies a person has experienced.
- Plasma glucose concentration: The blood glucose level measured 2 hours after an oral glucose tolerance test, providing insights into the body's ability to handle glucose.
- Diastolic blood pressure: The pressure in the arteries when the heart is at rest, measured in millimeters of mercury (mm Hg).
- Triceps skin fold thickness: The thickness of a fold of skin on the back of the arm, measured in millimeters, which can be an indicator of body fat.
- 2-Hour serum insulin: The insulin level in the blood measured 2 hours after consuming glucose, reflecting the body's insulin response.
- Body mass index (BMI): Calculated as the ratio of weight in kilograms to the square of height in meters, BMI is a measure of body fat and overall health.
- Diabetes pedigree function: A function that scores the likelihood of diabetes based on family history.
- Age: The age of the individual in years.
- Class variable (0 or 1): The target variable indicating the presence (1) or absence (0) of diabetes.
This dataset is valuable for training machine learning models, particularly those using supervised learning techniques, to predict and understand the factors associated with the likelihood of diabetes based on these health-related features.
Data preprocessing is a crucial step in preparing a dataset for machine learning models, involving various tasks such as splitting the data into training and testing sets and performing feature scaling. Here's an explanation of these two key aspects:
- Splitting the Data into Train and Test Sets:
- Objective: The primary goal is to evaluate the model's performance on unseen data and ensure that it generalizes well.
- Process: The dataset is divided into two subsets: a training set used to train the model, and a testing set used to evaluate its performance. A common split ratio is 80-20 or 70-30, where the majority of the data is used for training, and the remainder is reserved for testing.
- Note: The random_state parameter ensures reproducibility by fixing the random seed, resulting in the same split every time the code is run.
- Feature Scaling:
- Objective: Many machine learning algorithms are sensitive to the scale of features, so scaling ensures that all features contribute equally to the model's training.
- Process:
- Standardization (Z-score normalization): It transforms the data to have a mean of 0 and a standard deviation of 1. This is done by subtracting the mean and dividing by the standard deviation.
- Normalization (Min-Max scaling): It scales the data to a specific range (e.g., [0, 1]). Each value is subtracted by the minimum value and divided by the range (max-min).
- Note: The scaling parameters (mean, standard deviation, min, max) are computed from the training set and then applied to both the training and testing sets to avoid data leakage.
These preprocessing steps contribute to building more robust and accurate machine learning models by ensuring fair representation of features and reliable evaluation on unseen data.
The model undergoes 100 training epochs, meaning it iterates over the entire training dataset 100 times. During each epoch, the model adjusts its weights to minimize the specified loss function.
The loss (measured by binary crossentropy) gradually decreases over the epochs, indicating that the model is learning and improving its ability to make predictions. The accuracy steadily increases, reaching approximately 84.37% at the end of the training. This suggests that the model is becoming more adept at correctly classifying instances from the training data.
The final accuracy of 84.37% means that, on the training data, the model correctly predicted the presence or absence of diabetes in approximately 84.37% of cases. This percentage reflects the proportion of correct predictions relative to the total number of instances in the training set.
While the accuracy is a useful metric, it is important to consider the context of the problem. It might be beneficial to examine other metrics, such as precision, recall, or the area under the ROC curve, especially if the dataset is imbalanced. Additionally, evaluating the model on a separate validation set or test set is crucial to assess its generalization performance on unseen data.
The training seems relatively fast, with each epoch completing in a short amount of time (given the reported milliseconds per step). This suggests that the model is not overly complex, and the dataset may not be extremely large.
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and specific details that are unique to the training set but may not generalize well to new, unseen data. In essence, the model becomes too tailored to the idiosyncrasies of the training data, resulting in poor performance on validation or test datasets.
A lower model accuracy on the training set is often preferred to avoid overfitting. This might seem counterintuitive, but it signifies that the model is not memorizing the training data but rather learning the underlying patterns and features that are more likely to generalize to new data. A model with slightly lower training accuracy but better generalization capability is preferred because it is more likely to perform well on unseen data, demonstrating its ability to make accurate predictions in real-world scenarios beyond the training set. Regularization techniques, such as dropout and weight regularization, are commonly employed to help prevent overfitting and promote better generalization.
This project is licensed under the MIT License - see the LICENSE file for details.