Artificial Neural Networks in Drug Addiction Diagnosis

Engin Karaman

Makale detayları

Artificial Neural Networks in Drug Addiction Diagnosis

Yazarlar:

Engin Karaman

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

İstatistiksel Veri Bilimi

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1121-1126

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001346244

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 This study aims to find a simple mechanism to help researchers and families identify addicts. In this paper, the Artificial Neural Network ANN method has been examined to determine whether a person is an addict. In this study, the dataset obtained from students from different countries and published as open source by Atif Masih was used. This dataset contains 50343 samples with 11 features. The study involved testing and comparing multiple neural network architectures based on their average classification accuracy. When the correlation matrix is examined, it is seen that the relationships between the variables are almost negligible. This can be attributed to the fact that the variables are categorical. Each architecture was trained using 10 different seed numbers, and the mean accuracy was calculated accordingly. The experiment results have obtained 75.53 classification accuracy for correct diagnosis in our system. Our model could significantly expedite the diagnosis and treatment of addiction, providing a valuable tool for families, physicians, and investigators. The paper proposes a Decision Support System DSS for diagnosing addiction, leveraging one of the most widely-used machine learning techniques: Artificial Neural Networks ANN.

 This study aims to find a simple mechanism to help researchers and families identify addicts. In this paper, the Artificial Neural Network ANN method has been examined to determine whether a person is an addict. In this study, the dataset obtained from students from different countries and published as open source by Atif Masih was used. This dataset contains 50343 samples with 11 features. The study involved testing and comparing multiple neural network architectures based on their average classification accuracy. When the correlation matrix is examined, it is seen that the relationships between the variables are almost negligible. This can be attributed to the fact that the variables are categorical. Each architecture was trained using 10 different seed numbers, and the mean accuracy was calculated accordingly. The experiment results have obtained 75.53 classification accuracy for correct diagnosis in our system. Our model could significantly expedite the diagnosis and treatment of addiction, providing a valuable tool for families, physicians, and investigators. The paper proposes a Decision Support System DSS for diagnosing addiction, leveraging one of the most widely-used machine learning techniques: Artificial Neural Networks ANN.

 This study aims to find a simple mechanism to help researchers and families identify addicts. In this paper, the Artificial Neural Network ANN method has been examined to determine whether a person is an addict. In this study, the dataset obtained from students from different countries and published as open source by Atif Masih was used. This dataset contains 50343 samples with 11 features. The study involved testing and comparing multiple neural network architectures based on their average classification accuracy. When the correlation matrix is examined, it is seen that the relationships between the variables are almost negligible. This can be attributed to the fact that the variables are categorical. Each architecture was trained using 10 different seed numbers, and the mean accuracy was calculated accordingly. The experiment results have obtained 75.53 classification accuracy for correct diagnosis in our system. Our model could significantly expedite the diagnosis and treatment of addiction, providing a valuable tool for families, physicians, and investigators. The paper proposes a Decision Support System DSS for diagnosing addiction, leveraging one of the most widely-used machine learning techniques: Artificial Neural Networks ANN.

 This study aims to find a simple mechanism to help researchers and families identify addicts. In this paper, the Artificial Neural Network ANN method has been examined to determine whether a person is an addict. In this study, the dataset obtained from students from different countries and published as open source by Atif Masih was used. This dataset contains 50343 samples with 11 features. The study involved testing and comparing multiple neural network architectures based on their average classification accuracy. When the correlation matrix is examined, it is seen that the relationships between the variables are almost negligible. This can be attributed to the fact that the variables are categorical. Each architecture was trained using 10 different seed numbers, and the mean accuracy was calculated accordingly. The experiment results have obtained 75.53 classification accuracy for correct diagnosis in our system. Our model could significantly expedite the diagnosis and treatment of addiction, providing a valuable tool for families, physicians, and investigators. The paper proposes a Decision Support System DSS for diagnosing addiction, leveraging one of the most widely-used machine learning techniques: Artificial Neural Networks ANN.

Makale detayları

Artificial Neural Networks in Drug Addiction Diagnosis

Yazarlar:

Engin Karaman

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

İstatistiksel Veri Bilimi

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1121-1126

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001346244

pISSN:

2619-8991

Drug addiction

Artificial neural network

Decision support systemDrug addiction

Artificial neural network

Decision support system

Sayı

Makale detayları

Artificial Neural Networks in Drug Addiction Diagnosis

Yazarlar: Engin Karaman

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: İstatistiksel Veri Bilimi

Dergi: Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1121-1126

Belge Tipi: Makale

Yayın Tarihi: 7/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001346244

pISSN: 2619-8991

Drug addiction

Artificial neural network

Decision support systemDrug addiction

Artificial neural network

Decision support system

Sayı

Yazarlar:

Engin Karaman

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

İstatistiksel Veri Bilimi

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1121-1126

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001346244

pISSN:

2619-8991