Harmony-based feature selection to improve the nearest neighbor classification

Adeli, Ali; Sinaee, Mehrnoosh; Zomorodian, Javad; Neshat, Mehdi

doi:10.1145/2393216.2393219

Cited by 1 publication

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach described in the literature is used to construct 4320 inference rules in our fuzzy expert system [ 40 ]. Following that, we modified the Cleveland database’s detection rules for heart disease in the UCI repository.…”

Section: Methodsmentioning

confidence: 99%

“…A statistical link between the individual membership function of the input variable and output variable (heart disease status) is obtained using the methodology described in the literature [ 40 ]. In both the input and output variables, the middle point value of every linguistic term membership function is used.…”

Section: Methodsmentioning

confidence: 99%

“…Another group of researchers in [ 32 – 39 ] focuses on the diagnosis of heart diseases. With a different number of input and output attributes, authors in [ 40 – 48 ] present different levels of accuracy percentages. The achieved accuracy is 63.24% [ 47 ] to 94.05% [ 44 ] based on various input and output features implemented in Mamdani inference systems.…”

Section: Related Workmentioning

confidence: 99%

“…The achieved accuracy is 63.24% [ 47 ] to 94.05% [ 44 ] based on various input and output features implemented in Mamdani inference systems. Literature [ 40 ] displays 94% accuracy using 44 rules in the fuzzy expert system with eleven inputs and one output. The accuracy is increased a bit more (94.05%) in [ 44 ] as the work introduced decision tree algorithm with the fuzzy expert system.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Diagnosis of heart diseases: A fuzzy-logic-based approach

Ali,

Sadi,

Goni

2024

PLoS ONE

View full text Add to dashboard Cite

Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations