# Enhanced heart disease diagnosis and management: A multi-phase framework leveraging deep learning and personalized nutrition

## TL;DR

This paper introduces a deep learning framework for heart disease diagnosis and personalized nutrition recommendations, achieving high accuracy.

## Contribution

A novel multi-phase framework combining CNN, Inception Net, LSTM, and Angle DetectNet with DRL for personalized nutrition.

## Key findings

- The proposed CILAD-Net model achieved an accuracy of 0.998, outperforming existing models like DenseNet-201 and KNN.
- The framework integrates data preprocessing, feature extraction, and reinforcement learning for improved diagnosis and treatment personalization.

## Abstract

In health care, an accurate diagnosis with the help of a data-driven forecasting framework takes the risk factors associated with heart disease. However, building such an effective model using deep learning (DL) methods requires high-quality data, i.e., data free of outliers or anomalies. The current paper proposes a new approach to diagnosing and controlling heart diseases by utilizing a multi-tiered data acquisition model, data pre-processing, feature extraction, and DL. The framework encompasses four types of datasets. The first phase of the proposed methodology consists of data acquisition, while the second phase includes advanced data preprocessing for each data type. In phase three, multi-feature extraction methods are used to extract the features from the dataset. In phase four, a combined feature selection technique of ReliefF and Pearson correlation is used to select the best features. Phase five of the study is the formulation of the CILAD-Net DL model that integrates CNN, Inception Net, LSTM, and Angle DetectNet to accurately detect heart disease. The sixth phase implements Deep Reinforcement Learning (DRL) for nutrition recommendations based on the detected disease, thus improving the treatment individualization. The developed model’s experimental outcomes are validated with other prevailing models in terms of accuracy, recall, hamming loss, and so on. Finally, the outcomes of the proposed model attain the higher accuracy of 0. 998 for the CILAD-Net model, which is significantly better than DenseNet-201 with 0. 988, ANN with 0. 987, KNN with 0. 977, and CL-Net with 0. 984.