Öz
Osteoporoz, vücudumuzdaki kemiklerin sertliklerinin azalıp,
kalitelerinin bozulması sonucunda daha zayıf ve kırılabilir hale gelmeleri ile
ortaya çıkan ve tüm iskeletimizi etkileyen sistemik bir hastalıktır. Bu
çalışmada, bir iskelet hastalığı olan osteoporozun ön tanısında kullanılan X-ray
absorbsiyometri (DEXA) testinin radyasyon dezavantajı sebebiyle, buna
alternatif ve yapay zeka tabanlı, doğruluk değeri yüksek bir karar destek
sistemi oluşturmak amaçlanmıştır. Gerçekleştirilecek sistem bir ön tanı yöntemi
olarak kullanılacaktır. Bunun için, 70 hastadan alınan belirli parametrelerden
oluşturulan veri seti yardımı ile tasarlanan olasılıksal sinir ağı (OSA)
kullanılmıştır. Elde edilen başarı oranı ile Yapay sinir ağlarının osteoporoz
hastalığının teşhisinde karar destek sistemi olarak kullanılabileceği
görülmüştür. Bu çalışma sayesinde bu hastalığın şüphesi ile ilgili birime
gelecek tüm hastalara DEXA testinin uygulanma olasılığı aza indirgenmiş
olacaktır.
Anahtar Kelimeler
Osteoporozun Tanısı Olasılıksal Sinir Ağları Karar Destek Sistemleri
Kaynakça
- Alkan BM, Fidan F, Tosun A, Ardıçoğlu Ö. 2011. “Fiziksel Tıp ve rehabilitasyon polikliniğimize başvuran hastalarda osteoporoz insidansı”. Türk Osteoporoz Dergisi. vol-17:10-3.
- Aslan A, Karakoyun Ö, Güler E, Aydın S, Gök MV, Akkurt S. 2012. “Evaluation of bone mineral density, osteoporosis prevalence and regional risk factors in Turkish women living in Kastamonu: KASTÜRKOS study”. Eklem Hastalık Cerrahisi, vol-23(2):62-67.
- Baim S, Binkley N, Bilezikian JP, Kendler DL, Hans DB, Lewiecki EM, et al. 2008. “Official Positions of the International Society for Clinical Densitometry and executive summary of the 2007 ISCD Position Development Conference”. J Clin Densitom; vol-11:75-91.
- Boonen S, Vanderschueren D, Cheng XG, et al. 1997. “Age-related (type II) femoral neck osteoporosis in men: biochemical evidence for both hypovitaminosis D- and androgen deficiency-induced bone resorption”. J Bone Miner Res; vol-12:2119-26.
- Çelik O, Salcı Y, Manisalı M, Korkusuz F. 2009. “The effect of hip rotation on bone mineral density of the proximal femur measured by dual energy X-ray absorptiometry”. Eklem Hastalik Cerrahisi; vol-20:71-7.
- Delen D, Walker G, Kadam A. 2005. “Predicting breast cancer survivability: A comparison of three data mining methods”, Artificial Intelligence in Medicine, Vol.34, No.2,113-127.
- Er O, Sertkaya C, Temurtas F, Tanrikulu AC. 2009. “A comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis using neural networks and artificial immune system”, Journal of Medical Systems, Vol.33, No.66, 485-492.
- Er O, Tanrikulu AC, Abakay A. 2015. “Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma”. Dic Med J, vol-42(1), 5–11.
- Gul O, Atik OS, Erdogan D, Goktas G. 2012. “Is bone microstructure different between osteopenic and osteoporotic patients with femoral neck fracture?”. [Article in Turkish] Eklem Hastalik Cerrahisi. Vol-23:15-9.
- Gulbag A, Temurtas F, Yusubov I. 2007. “Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks”, Sens. Actuators B: Chem., Vol.131, 196-204.
- Pınar G, Pınar T, Doğan N, Karahan A, Algıer L, Abbasoğlu A, Kuşcu E. 2009. “Osteoporosis risk factors in the women over 45-years old”. Dicle Tıp Derg / Dicle Med J Cilt/Vol 36, No 4, 258-266.
- Specht DF. 1990. “Probabilistic neural networks”, Neural Networks, Vol.3, 109–118.
- Temurtas F. 2009. “A comparative study on thyroid disease diagnosis using neural networks”, Expert Systems with Applications, Vol.36, No.1, 944–949.
- World Health Organization Study Group: “Assessment of fracture risk and its application to screening for postmenopausal osteoporosis”. World Health Organ Tech Rep Ser 1994;843:1-129.
Öz
Osteoporosis
is a skeletal disorder characterized by low bone density and micro-architectural
deterioration of bony tissue. Dual-energy x-ray absorptiometry (DEXA) uses
x-ray beams at two photon energies to estimate bone mineral density (BMD). This
method has been applied extensively to detect osteoporosis. Due to the
radiation disadvantage of the DEXA test, alternatively, an artificial
intelligence-based decision support system was aimed. The system to be performed will be
used as a preliminary diagnosis method for osteoporosis. For this,
the probabilistic neural network (PNN) was used from 70 patient’s specific
parameters. It has been observed that artificial neural networks can be used as
a decision support system in the diagnosis of osteoporosis. Thanks to
this study, the probability of the application of the DEXA test will be reduced
to a minimum for all the patients who are suspected of having this disease.
Anahtar Kelimeler
Diagnosis of Osteoporosis Probabilistic Neural Networks Decision Support Systems
Kaynakça
- Alkan BM, Fidan F, Tosun A, Ardıçoğlu Ö. 2011. “Fiziksel Tıp ve rehabilitasyon polikliniğimize başvuran hastalarda osteoporoz insidansı”. Türk Osteoporoz Dergisi. vol-17:10-3.
- Aslan A, Karakoyun Ö, Güler E, Aydın S, Gök MV, Akkurt S. 2012. “Evaluation of bone mineral density, osteoporosis prevalence and regional risk factors in Turkish women living in Kastamonu: KASTÜRKOS study”. Eklem Hastalık Cerrahisi, vol-23(2):62-67.
- Baim S, Binkley N, Bilezikian JP, Kendler DL, Hans DB, Lewiecki EM, et al. 2008. “Official Positions of the International Society for Clinical Densitometry and executive summary of the 2007 ISCD Position Development Conference”. J Clin Densitom; vol-11:75-91.
- Boonen S, Vanderschueren D, Cheng XG, et al. 1997. “Age-related (type II) femoral neck osteoporosis in men: biochemical evidence for both hypovitaminosis D- and androgen deficiency-induced bone resorption”. J Bone Miner Res; vol-12:2119-26.
- Çelik O, Salcı Y, Manisalı M, Korkusuz F. 2009. “The effect of hip rotation on bone mineral density of the proximal femur measured by dual energy X-ray absorptiometry”. Eklem Hastalik Cerrahisi; vol-20:71-7.
- Delen D, Walker G, Kadam A. 2005. “Predicting breast cancer survivability: A comparison of three data mining methods”, Artificial Intelligence in Medicine, Vol.34, No.2,113-127.
- Er O, Sertkaya C, Temurtas F, Tanrikulu AC. 2009. “A comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis using neural networks and artificial immune system”, Journal of Medical Systems, Vol.33, No.66, 485-492.
- Er O, Tanrikulu AC, Abakay A. 2015. “Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma”. Dic Med J, vol-42(1), 5–11.
- Gul O, Atik OS, Erdogan D, Goktas G. 2012. “Is bone microstructure different between osteopenic and osteoporotic patients with femoral neck fracture?”. [Article in Turkish] Eklem Hastalik Cerrahisi. Vol-23:15-9.
- Gulbag A, Temurtas F, Yusubov I. 2007. “Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks”, Sens. Actuators B: Chem., Vol.131, 196-204.
- Pınar G, Pınar T, Doğan N, Karahan A, Algıer L, Abbasoğlu A, Kuşcu E. 2009. “Osteoporosis risk factors in the women over 45-years old”. Dicle Tıp Derg / Dicle Med J Cilt/Vol 36, No 4, 258-266.
- Specht DF. 1990. “Probabilistic neural networks”, Neural Networks, Vol.3, 109–118.
- Temurtas F. 2009. “A comparative study on thyroid disease diagnosis using neural networks”, Expert Systems with Applications, Vol.36, No.1, 944–949.
- World Health Organization Study Group: “Assessment of fracture risk and its application to screening for postmenopausal osteoporosis”. World Health Organ Tech Rep Ser 1994;843:1-129.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Bilgisayar Yazılımı |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 18 Aralık 2018 |
| Gönderilme Tarihi | 12 Aralık 2018 |
| Kabul Tarihi | 17 Aralık 2018 |
| Yayımlandığı Sayı | Yıl 2018Cilt: 1 Sayı: 3 |
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https://doi.org/10.35377/saucis.04.01.879735
Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science