Synergizing KNN and CNN for Robust Heart Disease Detection with Sequential Feature Selection

Problem Definition

The existing literature highlights the continuous efforts made by researchers to improve the early detection of cardiovascular diseases (CVDs) through various diagnostic techniques over the last few decades. While machine learning (ML) techniques were initially relied upon for CVD detection, the transition towards deep learning (DL) methods was driven by the need to effectively handle large datasets. Although DL models demonstrated efficiency in identifying CVDs, they were not without flaws. One key limitation observed in traditional CVD detection approaches was the inability of classifiers to adapt to changes in data caused by noise or other factors, making them unsuitable for sequential data analysis. Additionally, these methods required substantial amounts of training data to achieve optimal results.

The complexity of databases further compounded the challenges in CVD detection, rendering the process time-consuming and cumbersome. It is clear that there is a pressing need for an upgraded and effective CVD detection model that not only enhances accuracy rates but also reduces complexity and processing time to optimize patient outcomes and mortality rates.

Objective

The objective is to develop an upgraded and effective cardiovascular disease detection model by combining a sequential feature selection algorithm with a hybrid deep learning approach. This model aims to enhance accuracy rates, reduce complexity, and processing time to optimize patient outcomes and mortality rates. The proposed work involves preprocessing a dataset, selecting relevant features, and integrating KNN and CNN classifiers to improve disease classification accuracy and efficiency. The goal is to provide a comprehensive solution that surpasses the limitations of traditional methods for heart disease detection.

Proposed Work

By combining a sequential feature selection algorithm with a hybrid deep learning approach, this proposed work aims to address the limitations found in traditional heart disease detection methods. The objective is to enhance the accuracy of disease classification while reducing the complexity and processing time of the model. The initial step involves preprocessing a dataset obtained from the UCI Machine Learning repository, consisting of information from 303 patients with 75 attributes. Missing and null entries are handled to create a balanced and normalized dataset. The sequential feature selection method is then employed to choose relevant characteristics and eliminate unnecessary attributes, thus reducing the dataset's dimensionality and overall complexity.

This approach not only streamlines the detection system but also minimizes computational time. Furthermore, the integration of a hybrid deep learning approach using KNN and CNN classifiers is utilized to improve disease classification accuracy. By combining KNN for sequential data and CNN for large datasets, the model can effectively address the shortcomings of each individual classifier. This approach not only enhances the efficiency of heart disease diagnosis but also showcases the potential for increased accuracy in disease detection. The rationale behind this approach lies in the need to optimize the classification process by leveraging the strengths of each classifier while mitigating their individual weaknesses.

Overall, the proposed work aims to provide a comprehensive and effective solution for heart disease detection that surpasses the limitations of traditional methods, ultimately leading to improved diagnosis and treatment outcomes.

Application Area for Industry

This project can be applied in various industrial sectors that require effective and accurate disease detection systems, such as healthcare, pharmaceuticals, and medical technology. The proposed solutions provided in this project address the specific challenges faced by industries in detecting cardiovascular diseases early and accurately. By utilizing a feature selection technique to reduce dataset dimensionality and a hybrid DL approach for classification, this project offers benefits such as improved accuracy rates, reduced complexity, and decreased processing time in CVD detection models. Industries can leverage these advancements to enhance their disease detection systems, leading to better patient outcomes, reduced mortality rates, and more efficient healthcare delivery.

Application Area for Academics

The proposed project on improving heart disease detection through a hybridized DL approach has significant potential to enrich academic research, education, and training in several ways. Firstly, by addressing the shortcomings of traditional CVD detection methods, the project introduces innovative research methods and algorithms such as sequential feature selection, CNN, and KNN. This presents an opportunity for researchers, MTech students, and PHD scholars to explore new avenues in the field of healthcare analytics and machine learning. Moreover, the project's focus on reducing the complexity of datasets and enhancing classification accuracy can have a profound impact on educational settings. By implementing these advanced techniques, educators can train students on cutting-edge methods in data analysis and simulation, preparing them for real-world applications in healthcare and beyond.

The relevance of this project is demonstrated through its potential applications in various research domains, particularly in healthcare and medical diagnostics. Researchers specializing in machine learning, artificial intelligence, and healthcare analytics can utilize the code and literature from this project to enhance their own work in developing novel solutions for early disease detection. In terms of future scope, the project opens up possibilities for further exploration into hybrid DL approaches, feature selection techniques, and optimization algorithms. By continuing to refine and expand upon the current model, researchers can uncover new insights and advancements in the field of heart disease detection, leading to improved patient outcomes and healthcare practices.

Algorithms Used

The Sequential feature selection algorithm is used to reduce the dimensionality of the dataset by selecting critical and important characteristics while removing unwanted attributes. This helps in minimizing complexity and processing time in the heart disease detection model. The Convolutional Neural Network (CNN) and K-Nearest Neighbors (KNN) algorithms are utilized in a hybridized DL approach to improve the classification accuracy of the disease detection system. By combining KNN and CNN, the model leverages the strengths of each algorithm - KNN for sequential data and CNN for large datasets, leading to enhanced efficiency and accurate heart disease diagnosis.

Keywords

Heart disease prediction, CNN, Convolutional Neural Network, Deep learning, Medical diagnosis, Cardiology, Cardiovascular diseases, Risk assessment, Feature extraction, Data preprocessing, ECG analysis, Medical imaging, Health monitoring, Machine learning, Health risk prediction, Healthcare technology, Artificial intelligence, Sequential feature selection, Dimensionality reduction, Hybrid KNN-CNN classifiers, UCI Machine Learning repository, ML techniques, DL methods, CVD detection algorithms, Complexity reduction, Processing time minimization, Classification accuracy enhancement.

SEO Tags

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