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All Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Puspita Indah, Ratna
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Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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AUTOMATIC DIABETES DETECTION SYSTEM USING PCA AND FUZZY K-NN Permana Yudha, Ery; Purwanto, Eko; Puspita Indah, Ratna
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3392

Abstract

In recent years, the prevalence of diabetes has reached alarming levels, necessitating efficient and timely diagnosis for effective management. This research presents an innovative approach to diabetes detection through an automatic system that leverages advanced technologies such as machine learning and medical data analysis. The proposed system aims to streamline the diabetes diagnosis by analyzing various medical data sources. By utilizing a machine learning algorithm, the system seeks to extract meaningful patterns and relationships from these diverse datasets. Key components of the automatic diabetes detection system include data preprocessing, feature extraction, and model training. The system's performance is evaluated using various metrics, such as sensitivity, specificity, accuracy, and f1-score. Overall, the proposed automatic diabetes detection system holds immense promise in revolutionizing the field of diabetes diagnosis.
AUTOMATIC DIABETES DETECTION SYSTEM USING PCA AND FUZZY K-NN Permana Yudha, Ery; Purwanto, Eko; Puspita Indah, Ratna
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3392

Abstract

In recent years, the prevalence of diabetes has reached alarming levels, necessitating efficient and timely diagnosis for effective management. This research presents an innovative approach to diabetes detection through an automatic system that leverages advanced technologies such as machine learning and medical data analysis. The proposed system aims to streamline the diabetes diagnosis by analyzing various medical data sources. By utilizing a machine learning algorithm, the system seeks to extract meaningful patterns and relationships from these diverse datasets. Key components of the automatic diabetes detection system include data preprocessing, feature extraction, and model training. The system's performance is evaluated using various metrics, such as sensitivity, specificity, accuracy, and f1-score. Overall, the proposed automatic diabetes detection system holds immense promise in revolutionizing the field of diabetes diagnosis.
Co-Authors Eko Purwanto Permana Yudha, Ery