Research Papers - Dept of Computer Systems Engineering

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253

Browse

Filters

2017 - 201922020 - 20221

Advanced Search

Filter by

Settings

Subject: search.filters.subject.Forecasting

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationEmbargo
    Forecasting Model of Combining Mini Batch K Means and Kohonen Maps to Cluster and Evaluate Gait Kinematics Data
    (IEEE, 2022-10-04) Indumini, U; Jayakody, A
    When people are getting old, some gait abnormalities may have happened in their walking patterns. It means, there may be slight differences in their physical performance. Due to the complexity of that evaluation, a machine learning algorithm can be used to cluster the gait patterns. Kohonen Maps (KM) and mini-batch k-means (MBKM) have been combined to cluster the gait parameters according to the age groups to identify the principal gait characteristics which are affected to the walking pattern. Dataset is consisting of 180 gait data based on the data which have been gained through the inertial measurement unit (IMU). When analysing the results, the proposed algorithm is showing low computational cost and time which is more efficient. As well the results have been proved that the cadence is the most important and affected gait parameter when caused to a walking pattern of a person when he or she is getting older. These results provide clues for the health professionals to identify and evaluate the difficulties of walking patterns of patients according to age.
  • Thumbnail Image
    PublicationEmbargo
    Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD)
    (IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
    Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.
  • Thumbnail Image
    PublicationEmbargo
    Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD)
    (IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
    Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.