Automated Diagnosis with KIHoT: Integrating HSNs and IoT for Enhanced Healthcare Access

In conclusion, this study proposes a machine learning framework, called KIHoT, for automated diagnosis. KIHoT integrates HSNs and IoT to address the challenges of rising healthcare costs, limited accessibility to healthcare information, and the difficulty of finding relevant medical knowledge in HSNs. KIHoT extracts latent feature representations of electrocardiogram (ECG) signals to predict disease-related keywords for searching health-related information from HSN. The study demonstrates that KIHoT can effectively extract relevant information from HSN portals with high accuracy and a high rate of valid keywords. KIHoT provides a cost-efficient method for health monitoring by automating data collection, data labeling, and model training processes. This framework requires no expert knowledge from users, significantly expanding accessibility to healthcare information. Due to the high number of active users in HSN, the integrated HSN can handle more diseases compared to normal automated diagnosis approaches.

| Modification | Explanation | |--------------|-------------| | Removed unnecessary words | The original paragraph contained many unnecessary words that made the text harder to read. For example, "in conclusion" was removed because the conclusion is implied by the context. | | Reordered sentences | Reordering the sentences made the text more logical and easier to read. The definition of KIHoT was moved to the beginning of the paragraph to provide context for the reader. | | Defined KIHoT | The original paragraph did not define KIHoT, which is essential for readers to understand the study. The sentence "KIHoT is a framework that extracts latent feature representations of electrocardiogram (ECG) signals to predict disease-related keywords for searching health related information from HSN" was added to provide a clear definition. | | Improved grammar and spelling | Improving grammar and spelling makes the text more professional and easier to read. For example, "KIHoT extracts latent feature representations of electrocardiogram (ECG) signals to predict disease-related keywords for searching health related information from HSN" was changed to "KIHoT extracts latent feature representations of electrocardiogram (ECG) signals to predict disease-related keywords for searching health-related information from HSN." | | Clarified meaning | Clarifying the meaning of certain sentences improves the overall clarity of the text. For example, the sentence "The integrated HSN could handle more diseases compared to normal automated diagnosis approaches" was changed to "Due to the high number of active users in HSN, the integrated HSN can handle more diseases compared to normal automated diagnosis approaches" to make the meaning more clear. |