Deep Residual Learning for Early Prediction of Asthma Risk Factors

Manisha Bhatpahari; Nikita Rawat

Deep Residual Learning for Early Prediction of Asthma Risk Factors

Author(s)	Mrs. Manisha Bhatpahari, Nikita Rawat
Country	India
Abstract	Asthma is a long-term issue that can damage your lungs. It is a condition that affects your ability to breathe and impacts many people globally. Asthma can make it difficult for individuals to live life as they wish. Therefore, it is crucial to determine if someone has asthma as soon as possible so that doctors can help them feel better. Diagnosing asthma can be challenging because it shares many symptoms with other breathing problems. A correct diagnosis is vital to ensure that doctors provide the right treatment. This study is interesting because it uses the ResNet50 model to identify individuals at risk of developing asthma. The researchers examined various factors, including age, gender, family history of asthma, body mass index, and other health metrics like the FEV1/FVC ratio. They also considered allergies, air quality where people live, exposure to smokers, physical activity, and dietary habits. The ResNet50 model analyzes this data to find patterns that may not be obvious. This model can process a lot of information and uncover complex relationships. The researchers applied the ResNet50 model to study multiple physiological and lifestyle factors among a diverse group of people. They used machine learning classifiers like Random Forest, Logistic Regression, and XGBoost to evaluate the collected characteristics. This analysis helps to understand what these machine learning classifiers can reveal. Common metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are used to evaluate the model’s effectiveness. With a ROC-AUC of 98% and a prediction accuracy of 99%, this method outperforms traditional techniques. The findings indicate that by combining several key factors, deep residual learning paired with machine learning classifiers enhances asthma risk assessment and early detection, thus aiding clinical diagnosis and intervention.
Keywords	Respiratory diseases, pulmonary disease, asthma, artificial intelligence, clinical interventions
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-25

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Deep Residual Learning for Early Prediction of Asthma Risk Factors

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