AYODELE OLUWATOBI OLUWANIFEMI
DEVELOPMENT OF A PREDICTIVE SYSTEM FOR PREDICTING PREGNANCY COMPLICATIONS IN WOMEN USING MACHINE LEARNING ALGORITHMS - Dr. (Mrs.) Chinwe P. Igiri - Ibafo Computer Science and Mathematics 2022 - xi,; 43p.
This study is aimed at the application of predicting pregnancy complication with the use of different machine algorithms. The study achieved this by eliciting knowledge on the variables, collecting relevant data, formulating the classification model, simulating the
model. The use of different articles/studies were used as a guiding tools in the means of gathering the necessary data and features needed. The result of the study showed that out 11 variables, 7 variables were associated with the classification of predicting pregnancy complications. The result also identified a number of variables which had missing values from the data collected. The simulation showed that the higher the proportion of dataset used for training then the higher performance of the model on the testing dataset. So using 90% of the dataset for training and 10% for dataset for testing showed the highest performance.
Computer Science
DEVELOPMENT OF A PREDICTIVE SYSTEM FOR PREDICTING PREGNANCY COMPLICATIONS IN WOMEN USING MACHINE LEARNING ALGORITHMS - Dr. (Mrs.) Chinwe P. Igiri - Ibafo Computer Science and Mathematics 2022 - xi,; 43p.
This study is aimed at the application of predicting pregnancy complication with the use of different machine algorithms. The study achieved this by eliciting knowledge on the variables, collecting relevant data, formulating the classification model, simulating the
model. The use of different articles/studies were used as a guiding tools in the means of gathering the necessary data and features needed. The result of the study showed that out 11 variables, 7 variables were associated with the classification of predicting pregnancy complications. The result also identified a number of variables which had missing values from the data collected. The simulation showed that the higher the proportion of dataset used for training then the higher performance of the model on the testing dataset. So using 90% of the dataset for training and 10% for dataset for testing showed the highest performance.
Computer Science