Overview / Usage

To develop a heart disease prediction model, these algorithms are trained on labeled datasets containing patient data and corresponding heart disease labels. The dataset is divided into training and testing sets, and the algorithm is trained on the training set to learn patterns and relationships. The model's performance is then evaluated on the testing set to assess its predictive accuracy.

**The aim of the study is to provide a comparative study between the ****sklearn.linear\_model and sklearnex model.**

While scikit-learn itself is a community-driven open-source project, Intel has developed an optimized version of sklearn.linear\_model called sklearnex, which leverages Intel's performance libraries, including Intel(R) Math Kernel Library (Intel MKL). Intel MKL is a highly optimized library for mathematical computations, designed to take advantage of Intel's processors and accelerate computations.

The sklearnex module from Intel is built on top of scikit-learn and provides enhanced performance for linear models by utilizing Intel MKL's optimized linear algebra routines. It is specifically optimized for Intel processors, such as Intel Xeon and Intel Core processors, to deliver faster execution times for machine learning tasks involving linear models. By using sklearnex instead of the standard sklearn.linear\_model, you can benefit from the performance improvements provided by Intel MKL. These optimizations can lead to faster training and inference times for linear models, especially on Intel-based systems.It's important to note that while sklearnex can significantly improve the performance of linear models on Intel-based systems, it is not a drop-in replacement for sklearn.linear\_model. The usage and API of sklearnex are mostly consistent with sklearn.linear\_model, but there may be slight differences or additional features specific to sklearnex.

In summary, the **sklearnex** module from Intel is an optimized version of **sklearn.linear\_model**, leveraging Intel MKL's optimizations for linear algebra computations. By utilizing **sklearnex** on Intel-based systems, you can achieve faster execution times for linear models in scikit-learn, leading to improved performance in machine learning tasks.

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Methodology / Approach

1. Logistic Regression: This algorithm is often used for binary classification problems, making it suitable for predicting the presence or absence of heart disease based on patient features. Logistic regression models estimate the probability of a patient having heart disease based on the input variables.
2. Decsion Tree: Decsion tree is an ensemble learning algorithm, It can handle complex relationships between variables and can provide feature importance rankings, helping identify which factors contribute most to heart disease risk.

Technologies Used

sklearn.linear_model

sklearnex

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Repository

https://github.com/Ragzoid/oneAPI-Intel-Heart_disease_prediction