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<p>Diabetes detection requires evidence-based recommendations that enable people to manage their health. A rising diabetes rate can lead to significant health risks and financial hardships. An early diagnosis and efficient treatment are essential to reduce the effects of diabetes. Therefore, a novel Sensor-infused QUantum CNN for diabetes Identification and Diet recommendation (SQUID) technique has been proposed in this paper, which identifies diabetes in the early stage using an IoT system and provides diet recommendations for reducing diabetes. The proposed SQUID system collects data from remote patients using IoT sensors and uses the Namib Beetle Optimization (NBO) technique to select the features. The prediction phase uses the Quantum CNN technique for classifying the input into diabetes and non-diabetes. After prediction, the suggestion phase will provide the diet recommendation using the fuzzy rule for the person affected with diabetes through the mobile application. The efficacy of the proposed SQUID framework has been assessed using specific parameters such as Accuracy (AC), Precision (PN), F1 score (F1_S), Recall (RL) and Diagnostic Odds Ratio (DOR). The SQUID framework achieves a higher AC of 98.69%, whereas HCBDA, IWBSOA, e-diagnosis and GlucoBreath achieve the AC of 92%, 94%, 96.5% and 97.35% in the diabetes dataset. In the diabetes prediction dataset, the proposed SQUID model achieves a higher accuracy of 98.87%, whereas existing techniques such as HCBDA, IWBSOA, e-diagnosis and GlucoBreath achieve the AC of 91.35%, 93.56%, 97.21% and 96.43% in diabetes prediction dataset respectively.</p>

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Sensor Infused Quantum CNN for Diabetes Disease Prediction and Diet Recommendation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月12日

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  • Jameer Kotwal,
  • Pravin Futane,
  • Gurunath Chavan,
  • Archana Chaudhari,
  • Jithina Jose,
  • Vajid Khan

摘要

Diabetes detection requires evidence-based recommendations that enable people to manage their health. A rising diabetes rate can lead to significant health risks and financial hardships. An early diagnosis and efficient treatment are essential to reduce the effects of diabetes. Therefore, a novel Sensor-infused QUantum CNN for diabetes Identification and Diet recommendation (SQUID) technique has been proposed in this paper, which identifies diabetes in the early stage using an IoT system and provides diet recommendations for reducing diabetes. The proposed SQUID system collects data from remote patients using IoT sensors and uses the Namib Beetle Optimization (NBO) technique to select the features. The prediction phase uses the Quantum CNN technique for classifying the input into diabetes and non-diabetes. After prediction, the suggestion phase will provide the diet recommendation using the fuzzy rule for the person affected with diabetes through the mobile application. The efficacy of the proposed SQUID framework has been assessed using specific parameters such as Accuracy (AC), Precision (PN), F1 score (F1_S), Recall (RL) and Diagnostic Odds Ratio (DOR). The SQUID framework achieves a higher AC of 98.69%, whereas HCBDA, IWBSOA, e-diagnosis and GlucoBreath achieve the AC of 92%, 94%, 96.5% and 97.35% in the diabetes dataset. In the diabetes prediction dataset, the proposed SQUID model achieves a higher accuracy of 98.87%, whereas existing techniques such as HCBDA, IWBSOA, e-diagnosis and GlucoBreath achieve the AC of 91.35%, 93.56%, 97.21% and 96.43% in diabetes prediction dataset respectively.