Dynamic Model for Assisted Reproductive Technology Outcome Prediction

Infertility is becoming a growing issue in almost all countries. Assisted Reproductive Technologies (ART) are recent development in treating infertility that give hope to the infertile couples. However, the pregnancy rates achieved with the aid of ART is considerably low, as success in ART is not only based on the treatment but also on many other controllable and uncontrollable biological, social, and environmental features. High expenditures and painful process of ART cycles are the two major barriers for opting for ART. Moreover, ART treatments are not covered by any health insurance schemes. Computational prediction models could be used to improve the success rate by predicting the treatment outcome, before the start of an ART cycle. This may suggest the couples and the doctors to decide on the next course of action i.e. either to opt for ART or opt for correcting determinants or quit the ART. With the intension to improve the success rate of ART by providing decision support system to the physicians as well to the patients before entering into the treatment this research work proposes a dynamic model for ART outcome prediction using Machine Learning (ML) techniques. The proposed dynamic model is partially implemented with the help of an ensemble of heterogeneous incremental classifier and its performance is compared with state-of-art classifiers such as Naïve Bayes (NB), Random Forest (RF), K-star etc.,using ART dataset. Performance of the model is evaluated with various metrics such as accuracy, Precision Recall Curve (PRC), Receiver Operating Characteristic (ROC), F-Measure etc., However, ROC cure area is taken as the chief metric. Evaluation results shows that the model achieves the performance with the ROC area value of 94.1 %.

Keywords:
Assisted Reproductive Technology; Predictive Model; dynamic model; Machine Learning; Incremental Classifier

General factors	Hormone factor	Male factor
The general factors that are considered during treatment	Hormone levels that affects the chances of a couple achieving a pregnancy	Determinants relating to man that adversely affect the chances of a couple achieving a pregnancy.
1.Woman age 2.Man age 3.Duration of inf0ertility 4. Primary / secondary infertility 5.Body Mass Index (BMI) 6. Women Occupation 7. Men Occupation 8. Woman Education 9. Men Education	1.Follicle-stimulating hormone(FSH) 2. Luteinizing hormone (LH) 3. Prolactin 4. Dehydroepiandrosterone sulfate (DHEA-S) 5. Testosterone 6. Progesterone 7. Human chorionic gonadotropin (hCG) level - day 11 8. Anti- mullerian 9. Inhibin A 10. Thyroid Stimulating Hormone (TSH) 11. prolactin	1.Sperm quality 2. Total sperm count or sperm concentration 3. Morphology or normal forms 4. Motility or progressive motility 5. Quality of motility 6. Semen Volume 7. History of male urethritis 8.Varicocele 9. General Health 10. Allergies 11. Previous Treatment 12. Surgical Treatment
Female factor	IVF/ ICSI factors	Other factors
Determinants relating to woman that adversely affects the chances of a couple achieving a pregnancy.	Factors related to ART treatment that determines the chances of a couple achieving a pregnancy.	The other day-to-day activities that may affects the chances of a couple achieving a pregnancy
1. Ovarian factor 2. Tubal factor 3. Cervical factor (mucus) 4. History of Previous pregnancy / Complication 5. Previous childbirth 6. Ovarian size 7. Fibroid 8. Duration of ovarian stimulation 9. Endometriosis 10. Sub and intra endometrial vascular signals 11. Endometrium thickness 12. Endometrium morphology 13. Menstrual Cycle Detail 14. General Health 15. Fertility Medications 16. Surgical Treatment	1. Number of Previous Cycle 2. Number of embryos 3.Morphology score of the best and second best embryo 4.Fertilization rate 5.Method of fertilization 6.Ovulation Stimulation 7.Number of good quality embryos 8. Day of embryo transfer 9.Number of good quality embryos transferred 10.Number of retrieved oocytes 11.Number of pre-Ovulatory follicles 12.Proportion of fertilized oocytes 13. Physician performing Embryo Transfer 14. Number of frozen Embryo 15. Sperm Penetration assay 16. Outcome	1. Daily coffee 2. Smoking habits (current/former) 3. Alcohol 4. Tobacco 5. Stress measures 6. Relaxation method 7. Psychological Treatment 8. Exercise 9. Unknown factors 10. Residential Area 11. Family History 12. Weight Change 13. Others

Parameter Name	Available Values	Chosen Value	Description
Search Method	1. Linear Search 2. KD Tree 3. Ball Tree 4. Cover Tree	Linear Search	Used to searches the neighbouring instance
Distance Measure	1. Chebyshev distance 2. Euclidean distance 3. Manhattan distance 4. Minkowski distance	Euclidean distance	Used to find the similarity between the instances
Distance Weighting	1. 1/ distance 2. 1- distance	1/ distance	To give more influence on the predicted value for neighbors closer to the data point
Weighted A1DE	1. True 2. False	False	Weights are calculated based on mutual information between attribute and the class
Combination Rule	1. Average of Probability 2. Product of Probability 3. Majority Voting 4. Minimum Probability 5. Maximum Probability	Product of Probability	Shows how the classifier are combined together to produce the results

	Live Birth - P (1)(Predicted)	Not Live Birth - N(0)(Predicted)
Live Birth (Actual) - P (1)	True Positive (TP)	False Negative (FN)
Not Live Birth (Actual) - N(0)	False Positive (FP)	True Negative (TN)

Measure	Formula	Explanation
Accuracy	$\frac{(T P + T N)}{(T P + T N + F P + F N)}$	Ratio of correct predictions to total predictions.
True Positive Rate (TPR) / Recall / Sensitivity	$\frac{T P}{T P + F N}$	Measure of completeness or quantity
False Positive Rate (FPR) /(1 - Specificity)	$\frac{F P}{F P + T N}$	Measures the false alarm rate
True Negative Rate (Specificity)	$\frac{T N}{T N + F P}$	Proportion of actual negatives correctly identified as negatives
Precision / Positive Predictive Value	$\frac{T P}{T P + F P}$	Measure of exactness or quality. It means the percentage of the results which are relevant
F-Measure	$\frac{2 * Re c a l l * \Pr e c i s i o n}{Re c a l l + \Pr e c i s i o n}$	It is the harmonic average of the precision and recall.
Mean Absolute Error (MAE)	$\frac{\sum_{i = 1}^{n} a b s (y_{i} - x_{i})}{n}$	Measures the average of absolute errors in a set of predictions, where $y_{i}$ is the prediction and $x_{i}$ is the true value.
ROC	The Receiver Operating Characteristics (ROC) curve shows the relationship between Sensitivity and (1 - Specificity). The area under the ROC curve is ROC Area or Area Under ROC Curve (AUC).
PRC	A Precision-Recall Curve (PRC) is a plot of the precision (y-axis) and the recall (x-axis) for different thresholds, much like the ROC curve.

Classifier	Accuracy	ROC Area	Time (s)	MAE	PRC	F-Measure
PART	88.68	89.4	37.73	0.148	89.6	88.4
Naïve Bayes (NB)	76.24	78.6	0.03	0.25	82.9	76.3
Bayes Net	76.26	78.4	0.83	0.25	83	76.3
1-NN	87.75	90.9	0.01	0.13	91.7	87.0
Ada Boost (Decision Stump)	79.36	68.4	0.72	0.31	74.6	74.2
Random Forest (RF)	88	93.4	0.24	0.14	93.2	87.6
J48	86.69	83.8	1.23	0.19	85.6	86.0
Support Vector Machine	84.02	73.5	8255	0.15	76.1	82.6
Reduced Error Pruning (REP) Tree	85.7	83.7	0.91	0.20	85.5	84.9
LibSVM	80.7	64.4	161.56	0.19	70.7	76.6
Logistic Regression	84.5	86.9	746.43	0.22	89.3	83.4
Multilayer Perceptron (MLP)	76.2	73.5	1605.28	0.23	78.3	72.3
Proposed Model (EHIC)	85	94.1	0.2	0.15	94.1	84.7

Classifiers	Performance Metric (%)
Classifiers	Accuracy	ROC Area	F-Measure	PRC Area	Time (S) Taken	MAE
Stochastic Gradient Descent (SGD)	77.1	76.1	76.9	70.6	17.17	0.23
Radial Basis Function Network	77.89	77.6	0.87	76.5	0.31	76.7
Stochastic Primal Estimated sub-GrAdientSOlver for SVM (SPegasos)	77.6	75.5	76.9	70.8	24.02	0.2
Naïve Bayes (NB) Updatable	71.4	77.7	71.1	78.2	0.01	0.3
Local Weighted Learning (LWL)	67.06	74.7	60.6	76.0	0.02	0.41
K-Star	82.7	94	82.1	93.9	0.02	0.19
IB1	85.5	92.3	85.3	91.4	0.01	0.15
A1DE Updatable	77.2	84.4	76.2	84.8	0.12	0.26
Proposed Model (EHIC)	85	94.1	84.7	94.1	0.2	0.15

FS Method	Evaluator	Search Method	No. of Selected Attributes	Performance Metrics (%)
FS Method	Evaluator	Search Method	No. of Selected Attributes	Accuracy	F-Measure	ROC
Without FS	-	-	52	85	84.7	94.1
Filter Method	Correlated Feature Selection	Best First	7	73	71.2	75.3
		Greedy	7	73	71.2	75.3
		PSO	9	74.9	73.7	79.4
	Information Gain	Ranker	13	80.43	79.8	86.6
	Chi-Squared	Ranker	30	83.8	83.4	91.6
Wrapper Method	Proposed Model (EHIC)	Greedy	15	78.2	77.7	85.6
		Best First	15	78.2	77.7	85.6
		Incremental Wrapper Subset Selection (IWSS)	36	85.8	85.7	94.6
		IWSS Embedded with NB	18	81.1	80.4	87.8

Dataset Name	Number of Instance	Number of Attributes	Number of Classes
Prisoner	463	31	3
Vote	435	17	2
Breast Cancer	498	10	2
Hypothyroid	3772	30	4
Credit-g	1750	21	2

Dataset	Performance Metric (%)
Dataset	Accuracy	ROC Area	F-Measure	PRC Area	Time (s)	Error
Prisoner	97	99.9	97	99.8	0.001	0.02
Vote	93.6	98.7	93.6	98.8	0.001	0.06
Breast Cancer	87.6	94.2	87.6	94.0	0.04	0.13
Hypothyroid	93.8	94.0	92.8	95.9	0.03	0.03
Credit Card	85.9	96.3	85.6	95.7	0.01	0.14

Algorithm	Training		Classification		Ref
Algorithm	Time	Space	Time	Space
AIDE Updatable	$O (t n^{2})$	$O (k (n v^{2}))$	$O (k n^{2})$	$O (k (n v^{2}))$	[²⁵25 WebbG Boughton and Wang. Not so naive Bayes: aggregating one-dependence estimators.Mach. Learn.2005;58(1): 5-24.]
IB1	$O (t s)$	$O (s)$	$O (s)$	$O (s)$	[²⁶26 Wilson DR and Martinez TR. Reduction techniques for instance-based learning algorithms. Mach. Learn.2000; 38(3): 257-86.,²⁷27 Aha DW, Kibler D and Albert MK. Instance-based learning algorithms.Mach. Learn.,1991; 6(1):37-66.]
EHIC	$O (t n^{2}) + O (t s)$	$M a x (O (k (n v^{2})), O (s))$	$O (k n^{2}) + O (s)$	$M a x (O (k (n v^{2})), O (s))$

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[1] *Correspondence: ranjinirajalingam@gmail.com (R. K.).