The Review of Hybrid Machine Learning Models as a Promising Approach for Disease Diagnosis

Mgbeafulike Ike J; Nwokedi Chidiogo C; Adigwe; A I

doi:10.56201/ijcsmt.vol.11.no7.2025.pg39.54

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The Review of Hybrid Machine Learning Models as a Promising Approach for Disease Diagnosis

Mgbeafulike Ike J, Nwokedi Chidiogo C, Adigwe, A I

Abstract

The use of hybrid machine learning models has gained significant attention in recent years due to the potential it holds in diagnosis. This is especially valuable in the healthcare industry, where timely and accurate disease detection can greatly impact treatment and outcomes for patients. Hybrid models combine the strengths of different machine learning algorithms to overcome the limitations of individual methods allowing for more accurate and efficient disease detection. The versatility of hybrid models allows for the integration of various types of data, such as clinical, genetic, and imaging data. This enables a more comprehensive analysis of disease patterns and leads to more accurate and timely detection. In recent studies, hybrid models have been successfully applied in the detection of various diseases, including cancer, cardiovascular diseases, and infectious diseases. The potential of hybrid machine learning models in disease detection is undeniable. With their ability to handle large and diverse datasets, provide accurate predictions, and adapt to new data, these models have shown promising results and have the potential to greatly impact the field of medicine. Despite the promising results, there are still challenges that need to be addressed in the development and implementation of hybrid machine learning models for disease detection. These include complexity in modal integration, increase in computational cost and complex pre-processing pipeline. In conclusion, hybrid machine learning models have the potential to revolutionize disease detection and significantly improve patient outcomes. With continuous advancements in technology and increasing availability of diverse datasets, these models have the potential to revolutionize the way we detect and diagnose diseases in the future.

Keywords

Machine learning (ML)Hybrid ModelDetectiondiagnosisdataset

References

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The Review of Hybrid Machine Learning Models as a Promising Approach for Disease Diagnosis

Abstract

Keywords

References

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