Overview / Usage

It is a machine learning application that predicts the health status of a fetus using three different classification algorithms: logistic regression, SVM, and KNN. The goal of this project is to accurately classify fetuses as healthy or unhealthy based on various parameters such as fetal heart rate, uterine contractions, and other measurements.

The problem being solved by your project is the need for accurate and timely diagnosis of fetal health status, which is crucial for ensuring the well-being of both the mother and the baby. Early detection of fetal distress can help healthcare providers take timely interventions to prevent adverse outcomes such as fetal distress, hypoxia, or even stillbirth.

In production, your application can be used by healthcare providers such as obstetricians, midwives, and nurses to monitor fetal health during pregnancy and labor. By inputting the relevant data into the application, healthcare providers can get a quick and accurate assessment of the fetal health status and take appropriate actions to ensure the safety of the mother and the baby.

Overall, your project can have a significant impact on improving maternal and fetal health outcomes and can be a valuable tool for healthcare providers in managing high-risk pregnancies.

Methodology / Approach

The methodology for your project involves using machine learning algorithms to analyze and classify fetal health data. The project uses the oneDAL library from Intel to implement the classification algorithms. The following frameworks, standards, and techniques are used in the development of the project:

1. Machine learning algorithms: The project uses three different classification algorithms - logistic regression, SVM, and KNN - to analyze and classify the fetal health data. Each algorithm has its own strengths and weaknesses, and by using multiple algorithms, the project can compare their performance and choose the one that works best for the specific dataset and problem.
2. Data preprocessing: Before feeding the data into the machine learning algorithms, it needs to be preprocessed. This involves cleaning the data, removing any missing values or outliers, and normalizing the data to ensure that the features are on the same scale.
3. Feature engineering: Feature engineering involves selecting the most relevant features from the dataset that can help the machine learning algorithms make accurate predictions. The features used in the project may include fetal heart rate, uterine contractions, and other measurements that are known to be indicators of fetal health.
4. Evaluation metrics: To evaluate the performance of the machine learning algorithms, the project uses various evaluation metrics such as accuracy, precision, recall, and F1 score. These metrics provide a measure of how well the algorithms are able to classify the fetal health data.
5. oneDAL library: The oneDAL library from Intel provides efficient and scalable implementations of common machine learning algorithms such as logistic regression, SVM, and KNN. By using the oneDAL library, the project can take advantage of the optimized code and parallel processing capabilities, which can speed up the training of the machine learning models.

Overall, the project's approach involves using machine learning algorithms and data preprocessing techniques to analyze and classify fetal health data. The use of the oneDAL library from Intel can help speed up the training of the models and provide efficient implementations of common machine learning algorithms.

Technologies Used

Technologies:

• Machine learning algorithms (logistic regression, SVM, KNN)
• Data preprocessing techniques
• Feature engineering techniques
• Evaluation metrics (accuracy, precision, recall, F1 score)

Libraries:

• oneDAL library from Intel for efficient and scalable implementations of machine learning algorithms

Tools:

• Python programming language for data processing, machine learning modeling, and visualization
• Jupyter Notebook for interactive data analysis and model training

Software:

• Anaconda distribution for Python to manage packages and environments

Hardware:

• Computer with a suitable processor, RAM, and storage capacity to handle the data processing and machine learning tasks.

Intel technologies:

• oneDAL library for efficient and scalable implementations of machine learning algorithms.

Overall, the project leverages machine learning algorithms and techniques, along with the oneDAL library from Intel, to develop an application that can predict the health status of a fetus. Python programming language and Jupyter Notebook are used as the main tools for data analysis and modeling, while Anaconda distribution helps manage packages and environments. The project can be run on any suitable hardware configuration with enough processing power and storage capacity.

Other links

• oneDAL Git link (https://github.com/AkshayRamakrishnann/classification_oneDAL)