Cataract Disease Severity Prediction: A Hybrid CNN and Random Forest Strategy

The study's abstract provides a thorough analysis of the model's effectiveness in classifying various disease intensities. A structured table providing essential indicators for each degree of disease intensity is used to present this summary and divides the data into groups. The precision ratings, which range between 86.22% to 96.74%, demonstrate how well the models can produce positive predictions that are correct over a broad range of disease categories. This indicates that models are proficient in accurately recognizing cases with the given level of illness severity. The models' efficacy in identifying true positive cases is further demonstrated by the recall scores, which range between 87.94% to 92.79%. This further highlights the models' capacity for illness identification. The F1 results show that overall model performance falls between 88.91% and 93.29%. These ratings take into account both precision and recall. This range shows a well-balanced performance, achieving a balance between the models' capacity to detect genuine positive cases accurately and prevent false positives. Apart from these performance indicators, the table offers information on support proportions and values. These numbers show the distribution and make-up of the dataset that was used to assess the models. After a thorough evaluation with a dataset of 4628 photos, the models achieved an astounding 91.18% overall accuracy. Moreover, the model's ability to correctly classify disease intensity is highlighted by the weighted average accuracy of 91.21%, highlighting the importance of precision and recall as crucial performance indicators. Finally, the abstract summarizes the key findings of the study clearly and concisely, demonstrating the models' strong performance in accurately and successfully classifying a wide range of disease intensities, which makes them useful instruments for disease diagnosis.