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Research Article
SITI FAIRUZ MOHD RADZI
Universiti Sains Malaysia
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.
Result: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, and Deep Neural Network (DNN) algorithms to ASD screening dataset and compared the classifiers’ based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic (ROC), area under the curve (AUC), and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn will impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.
Conclusion: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.
Keywords
data mining, healthcare, Autistic Spectrum Disorder, classification, missing values
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No competing interests reported.
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 02 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 28 Sep, 2021
Reviews received
Received 15 Sep, 2021
Reviewers agreed
On 14 Sep, 2021
Reviewers invited
Invitations sent on 28 Apr, 2021
Editor assigned
On 20 Apr, 2021
Editor invited
On 29 Jan, 2021
Submission checks complete
On 29 Jan, 2021
First submitted
On 26 Jan, 2021
Metrics
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Subject Areas
Bioinformatics
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A new preprint design is coming!
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This preprint is under consideration at BMC Medical Informatics and Decision Making. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
SITI FAIRUZ MOHD RADZI
Universiti Sains Malaysia
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 02 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 28 Sep, 2021
Reviews received
Received 15 Sep, 2021
Reviewers agreed
On 14 Sep, 2021
Reviewers invited
Invitations sent on 28 Apr, 2021
Editor assigned
On 20 Apr, 2021
Editor invited
On 29 Jan, 2021
Submission checks complete
On 29 Jan, 2021
First submitted
On 26 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.
Result: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, and Deep Neural Network (DNN) algorithms to ASD screening dataset and compared the classifiers’ based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic (ROC), area under the curve (AUC), and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn will impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.
Conclusion: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
No competing interests reported.
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