Computer-Aided Disease Diagnosis Prediction Using Scalogram Images Obtained From Graded Scales Applied To Adolescents With Psychiatric Illness

Sinan Altun; Hatice Altun

Makale detayları

Computer-Aided Disease Diagnosis Prediction Using Scalogram Images Obtained From Graded Scales Applied To Adolescents With Psychiatric Illness

Yazarlar:

Sinan Altun | Hatice Altun

Yayıncı:

Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar:

Derin Öğrenme

Dergi:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1572-1597

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001354223

pISSN:

2687-3729

Anahtar kelimeler:

Özet:

 Adolescence is a difficult period for both adolescents and their families. Adolescents are sad and pessimistic. Adolescents may also experience outbursts of anger from time to time. Adolescents need above all to feel understood and valued. Otherwise, adolescents need another environment to satisfy these feelings. Adolescence is a difficult period of life and a psychologically challenging time for the individual and the family. If psychiatric illnesses during adolescence are left untreated, adolescents may suffer from permanent mental disorders. These disorders may continue by increasing the psychological disturbance of the person. Youth is an important factor in the development of a country in every field. For this reason, adolescence should be managed appropriately and a rapid diagnosistreatment process should be applied when a psychiatric illness occurs. Diagnosis of mental illnesses is also based on expert observation and requires good expertise. Of course, these systems are decision support systems and the final decision is left to the experts. In this study, we use machine learning to investigate the automatic treatment of mental illnesses in the challenging life stages of adolescence. Random Forest and Support Vector Machines algorithms, which are frequently used in the literature, are used. In these algorithms, higher classification success was obtained in scalogram images compared to the unprocessed data set. Random Forest: 91, Support Vector Machines: 88.

 Adolescence is a difficult period for both adolescents and their families. Adolescents are sad and pessimistic. Adolescents may also experience outbursts of anger from time to time. Adolescents need above all to feel understood and valued. Otherwise, adolescents need another environment to satisfy these feelings. Adolescence is a difficult period of life and a psychologically challenging time for the individual and the family. If psychiatric illnesses during adolescence are left untreated, adolescents may suffer from permanent mental disorders. These disorders may continue by increasing the psychological disturbance of the person. Youth is an important factor in the development of a country in every field. For this reason, adolescence should be managed appropriately and a rapid diagnosistreatment process should be applied when a psychiatric illness occurs. Diagnosis of mental illnesses is also based on expert observation and requires good expertise. Of course, these systems are decision support systems and the final decision is left to the experts. In this study, we use machine learning to investigate the automatic treatment of mental illnesses in the challenging life stages of adolescence. Random Forest and Support Vector Machines algorithms, which are frequently used in the literature, are used. In these algorithms, higher classification success was obtained in scalogram images compared to the unprocessed data set. Random Forest: 91, Support Vector Machines: 88.

 Adolescence is a difficult period for both adolescents and their families. Adolescents are sad and pessimistic. Adolescents may also experience outbursts of anger from time to time. Adolescents need above all to feel understood and valued. Otherwise, adolescents need another environment to satisfy these feelings. Adolescence is a difficult period of life and a psychologically challenging time for the individual and the family. If psychiatric illnesses during adolescence are left untreated, adolescents may suffer from permanent mental disorders. These disorders may continue by increasing the psychological disturbance of the person. Youth is an important factor in the development of a country in every field. For this reason, adolescence should be managed appropriately and a rapid diagnosistreatment process should be applied when a psychiatric illness occurs. Diagnosis of mental illnesses is also based on expert observation and requires good expertise. Of course, these systems are decision support systems and the final decision is left to the experts. In this study, we use machine learning to investigate the automatic treatment of mental illnesses in the challenging life stages of adolescence. Random Forest and Support Vector Machines algorithms, which are frequently used in the literature, are used. In these algorithms, higher classification success was obtained in scalogram images compared to the unprocessed data set. Random Forest: 91, Support Vector Machines: 88.

 Ergenlik dönemi hem ergenler hem de aileleri için zor bir dönemdir. Ergenler üzgün ve karamsardır. Ayrıca ergenler zaman zaman öfke patlamaları yaşayabilirler. Ergenler her şeyden önce anlaşıldıklarını ve kendilerine değer verildiğini hissetmeye ihtiyaç duyarlar. Aksi takdirde, ergenler bu duygularını tatmin etmek için başka bir ortama ihtiyaç duyarlar. Ergenlik, yaşamın zor bir dönemidir ve birey ve aile için psikolojik olarak zorlayıcı bir dönemdir. Ergenlik dönemindeki psikiyatrik hastalıklar tedavi edilmezse, ergenler kalıcı ruhsal bozukluklara maruz kalabilir. Bu bozukluklar kişinin psikolojik rahatsızlığını artırarak devam edebilir. Gençlik, bir ülkenin her alanda gelişmesinde önemli bir faktördür. Bu nedenle ergenlik dönemi uygun şekilde yönetilmeli ve psikiyatrik bir hastalık ortaya çıktığında hızlı bir tanıtedavi süreci uygulanmalıdır. Ruhsal hastalıkların teşhisi de uzman gözlemine dayanır ve iyi bir uzmanlık gerektirir. Tabii ki bu sistemler karar destek sistemleridir ve son karar uzmanlara bırakılır. Bu çalışmada, ergenlik döneminin zorlu yaşam evrelerinde akıl hastalıklarının otomatik tedavisi için makine öğrenimini araştırmak üzere kullanıyoruz. Literatürde sıkça kullanılan, Random Forest ve Support Vector Machines algoritmaları ile çalışılmıştır. Bu algoritamalarda, işlenmeden kullanılan veri setine göre Scalogram görüntülerinde daha yüksek sınıflandırma başarısı elde edilmiştir. Random Forest: 91, Support Vector Machines: 88.

Makale detayları

Computer-Aided Disease Diagnosis Prediction Using Scalogram Images Obtained From Graded Scales Applied To Adolescents With Psychiatric Illness

Yazarlar:

Sinan Altun | Hatice Altun

Yayıncı:

Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar:

Derin Öğrenme

Dergi:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1572-1597

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001354223

pISSN:

2687-3729

Adolescence

psychiatric disorders in adolescence

machine learning

automatic disease diagnosis predictionAdolescence

psychiatric disorders in adolescence

machine learning

automatic disease diagnosis prediction

Sayı

Makale detayları

Yazarlar: Sinan Altun | Hatice Altun

Yayıncı: Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar: Derin Öğrenme

Dergi: Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1572-1597

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001354223

pISSN: 2687-3729

Sayı

Yazarlar:

Sinan Altun | Hatice Altun

Yayıncı:

Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar:

Derin Öğrenme

Dergi:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1572-1597

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001354223

pISSN:

2687-3729