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<p>In the contemporary era, cardiovascular diseases (CVD) stand as the predominant global cause of mortality. Effectively preserving lives following a cardiovascular episode poses a significant challenge. The key to salvaging a patient’s life lies in the precise analysis and pre-determination of the disease before it inflicts severe damage. Various ongoing studies aim to identify heart disease in its nascent stages. This study reviews traditional and modern machine-learning techniques to predict heart disease, including MRI, ECG, and others. Despite advanced technology, precise diagnoses remain challenging for physicians due to minor data differences. Machine learning (ML) methods such as artificial neural networks (ANN), random forest, and CNN are employed to detect subtle distinctions in heart patient data. GAIT parameters like walking pattern and speed and ML identify minor changes, enabling early heart disease risk prediction. This predictive approach, validated by traditional methods, allows timely intervention and treatment in the early stages.</p> Graphical abstract <p>Heart Health Prediction using GAIT Parameter (data Collection, modelling and prediction)</p> <p></p>

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State of Art Review on Human Heart Health with and Without GAIT Parameter Using Data and Image-Based Deep Learning Model Approach

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Prabhat Singh Kourav,
  • Pawan Singh,
  • Vikash Kumar,
  • Subrata Kumar Panda

摘要

In the contemporary era, cardiovascular diseases (CVD) stand as the predominant global cause of mortality. Effectively preserving lives following a cardiovascular episode poses a significant challenge. The key to salvaging a patient’s life lies in the precise analysis and pre-determination of the disease before it inflicts severe damage. Various ongoing studies aim to identify heart disease in its nascent stages. This study reviews traditional and modern machine-learning techniques to predict heart disease, including MRI, ECG, and others. Despite advanced technology, precise diagnoses remain challenging for physicians due to minor data differences. Machine learning (ML) methods such as artificial neural networks (ANN), random forest, and CNN are employed to detect subtle distinctions in heart patient data. GAIT parameters like walking pattern and speed and ML identify minor changes, enabling early heart disease risk prediction. This predictive approach, validated by traditional methods, allows timely intervention and treatment in the early stages.

Graphical abstract

Heart Health Prediction using GAIT Parameter (data Collection, modelling and prediction)