The PCOS Detection ML model aims to address the pressing issue of Polycystic Ovary Syndrome (PCOS), a common hormonal disorder affecting approximately 10% of women of reproductive age. By leveraging machine learning algorithms and clinical data, our model strives to improve early detection and treatment of PCOS, ultimately enhancing healthcare outcomes and the quality of life for individuals impacted by the syndrome.
Taken from 44 hospitals located in kerala
AGE , WEIGHT , HEIGHT , BMI, BLOOD GROUP , PULSE RATE , RR SIZE
FAST FOOD INTAKE , REGULAR EXERCISE ,BLOOD PRESSURE , FOLLICLE NUMBER, AVG F SIZE , ENDOMETRIUM SIZE
1 => PCOS DETECTED
0 => NORMAL
CATBOOST CLASSIFIER =>100%
DECISION TREE => 82 %
SVC => 63%
RANDOM FOREST => 85%
LOGISTIC REGRESSION => 81%
K NEAREST NEIGHBOURS =>64%
XGBRF => 86%